Cogprints: No conditions. Results ordered -Date, Title. 2018-01-17T14:23:29ZEPrintshttp://cogprints.org/images/sitelogo.gifhttp://cogprints.org/2014-08-24T21:07:54Z2015-04-20T11:40:42Zhttp://cogprints.org/id/eprint/9760This item is in the repository with the URL: http://cogprints.org/id/eprint/97602014-08-24T21:07:54ZMotor Learning Mechanism on the Neuron Scale Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron’s firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and “grandmother cell” coding. And they are the kernel mechanisms of motor learning and sensory-motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models. Mr. Peilei Liulpl1520@163.comProf. Ting Wangtingwang1970@163.com2014-08-24T21:08:27Z2015-04-20T11:40:52Zhttp://cogprints.org/id/eprint/9753This item is in the repository with the URL: http://cogprints.org/id/eprint/97532014-08-24T21:08:27ZA Quantitative Neural Coding Model of Sensory Memory
The coding mechanism of sensory memory on the neuron scale is one of the most
important questions in neuroscience. We have put forward a quantitative neural network model,
which is self-organized, self-similar, and self-adaptive, just like an ecosystem following
Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from
objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with
powerful representing and learning ability. This model can reconcile some important disputations,
such as: temporal coding versus rate-based coding, grandmother cell versus population coding,
and decay theory versus interference theory. And it has also provided explanations for some key
questions such as memory consolidation, episodic memory, consciousness, and sentiment.
Philosophical significance is indicated at last.
PHD Peilei Liulpl1520@163.comProfessor Ting Wangtingwang1970@163.com2017-02-18T20:30:45Z2017-02-18T20:30:45Zhttp://cogprints.org/id/eprint/9814This item is in the repository with the URL: http://cogprints.org/id/eprint/98142017-02-18T20:30:45ZA Survey on Job and Task Scheduling in Big DataBigdata handles the datasets which exceeds the ability of commonly used software tools for storing, sharing and processing the data. Classification of workload is a major issue to the Big Data community namely job type evolution and job size evolution. On the basis of job type, job size and disk performance, clusters are been formed with data node, name node and secondary name node. To classify the workload and to perform the job scheduling, mapreduce algorithm is going to be applied. Based on the performance of individual machine, workload has been allocated. Mapreduce has two phases for processing the data: map and reduce phases. In map phase, the input dataset taken is splitted into keyvalue pairs and an intermediate output is obtained and in reduce phase that key value pair undergoes shuffle and sort operation. Intermediate files are created from map tasks are written to local disk and output files are written to distributed file system of Hadoop. Scheduling of different jobs to different disks are identified after completing mapreduce tasks. Johnson algorithm is used to schedule the jobs and used to find out the optimal solution of different jobs. It schedules the jobs into different pools and performs the scheduling. The main task to be carried out is to minimize the computation time for entire jobs and analyze the performance using response time factors in hadoop distributed file system. Based on the dataset size and number of nodes which is formed in hadoop cluster, the performance of individual jobs are identified
Keywords —
hadoop; mapreduce; johnson algorithmMr. MALCOM MARSHALL ALEXANDERmalcom.research@gmail.com2014-05-10T00:07:27Z2014-05-10T00:07:27Zhttp://cogprints.org/id/eprint/9223This item is in the repository with the URL: http://cogprints.org/id/eprint/92232014-05-10T00:07:27ZStructure and Dynamics in Implementation of ComputationsWithout a proper restriction on mappings, virtually any
system could be seen as implementing any computation. That
would not allow characterization of systems in terms of
implemented computations and is not compatible with a
computationalist philosophy of mind. Information-based criteria for independence of substates within structured states are proposed as a solution. Objections to the use of requirements for transitions in counterfactual states are addressed, in part using the partial-brain argument as a general counterargument to neural replacement arguments.Dr. Jacques Mallahjackmallah@yahoo.com2014-02-25T12:35:31Z2014-02-25T12:35:31Zhttp://cogprints.org/id/eprint/9126This item is in the repository with the URL: http://cogprints.org/id/eprint/91262014-02-25T12:35:31ZCellular-Automata and Innovation within Indonesian Traditional Weaving CraftsThe paper reports the possibility of Indonesian traditional artisans of weaving designs and crafts to explore the cellular automata, a dynamical model in computation that may yield similar patterns. The reviews of the cellular automata due to the perspective of weaving process reveals that the latter would focus on macro-properties, i.e.: the strength of structural construction beside the aesthetic patterns and designs. The meeting of traditional weaving practice and the computational model is delivered and open the door for interesting discourse of computer-aided designs for the traditional artists and designers to come. Hokky Situngkir2013-09-17T14:30:27Z2013-09-17T14:30:27Zhttp://cogprints.org/id/eprint/9091This item is in the repository with the URL: http://cogprints.org/id/eprint/90912013-09-17T14:30:27ZDynamics of the Corruption Eradication in IndonesiaThe paper discusses an important aspect of the complexity of corruption eradication in Indonesia. Corruption eradication is practically not merely about law enforcement, but also related to social, economic, and political aspects of the nation. By extracting the data from national news media and implement models describing the sentiment relations among political actors, the connection between balance of the sentiment among political elites and the critical levels of the investigation and law enforcement is apparently demonstrated. The focus group discussions among experts, practitioners, and social activists confirm the model. Hokky Situngkirhs@compsoc.bandungfe.netArdian Maulanaai@compsoc.bandungfe.net2013-05-04T23:07:10Z2013-05-04T23:07:10Zhttp://cogprints.org/id/eprint/8905This item is in the repository with the URL: http://cogprints.org/id/eprint/89052013-05-04T23:07:10ZModel Prediction-Based Approach to Fault Tolerant Control with ApplicationsAbstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information
is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the
fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions.Professor Magdi S. MahmoudDr. Haris M. Khalidharism.khaid@yahoo.com2013-05-04T23:06:41Z2013-05-04T23:06:41Zhttp://cogprints.org/id/eprint/8906This item is in the repository with the URL: http://cogprints.org/id/eprint/89062013-05-04T23:06:41ZBibliographic Review on Distributed Kalman FilteringIn recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area.Professor Magdi S. MahmoudDr. Haris M. Khalid harism.khalid@yahoo.com2013-05-04T23:07:22Z2013-05-04T23:07:22Zhttp://cogprints.org/id/eprint/8909This item is in the repository with the URL: http://cogprints.org/id/eprint/89092013-05-04T23:07:22ZDistributed Estimation Based on Prior InformationIn this paper, we propose an approach for distributed estimation algorithm using Bayesian-based forward backward (FB) Kalman filter (KF) used on a stochastic singular linear system. The approach incorporates generalized versions of KF presented for cases with complete or incomplete a-priori information with bounds, followed by estimation fusion for these cases. The proposed approach is then validated on a coupled tank system to ensure its effectiveness.Professor Magdi S. MahmoudDr. Haris M. Khalidharism.khalid@yahoo.com2012-11-09T19:41:42Z2012-11-09T19:41:42Zhttp://cogprints.org/id/eprint/8279This item is in the repository with the URL: http://cogprints.org/id/eprint/82792012-11-09T19:41:42ZOn Social and Economic Spheres: An Observation of the “gantangan” Indonesian tradition
Indonesian traditional villagers have a tradition for the sake of their own social and economic security named “nyumbang”. There are wide variations of the traditions across the archipelago, and we revisit an observation to one in Subang, West Java, Indonesia. The paper discusses and employs the evolutionary game theoretic insights to see the process of “gantangan”, of the intertwining social cohesion and economic expectation of the participation within the traditional activities. The current development of the “gantangan” tradition is approached and generalized to propose a view between the economic and social sphere surrounding modern people. The interaction between social and economic sphere might be seen as a kind of Lokta-Volterra’s predator-prey-like interaction, where both are conflicting yet in a great necessity one another for the sustainability of the social life. While some explanations due to the current development of “gantangan” is drawn, some aspects related to traditional views complying the modern life with social and economic expectations is outlined. Hokky SitungkirYanu Endar Prasetyo2012-04-25T12:29:38Z2012-04-25T12:29:38Zhttp://cogprints.org/id/eprint/8149This item is in the repository with the URL: http://cogprints.org/id/eprint/81492012-04-25T12:29:38ZCategory of Metabolic-Replication Systems
in Biology and MedicineMetabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs) in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life’s origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.Prof.Dr. I.C. Baianuibaianu@illinois.edu2012-04-25T12:30:10Z2012-04-25T12:30:10Zhttp://cogprints.org/id/eprint/8144This item is in the repository with the URL: http://cogprints.org/id/eprint/81442012-04-25T12:30:10ZQuantum Genetics and Quantum Automata Models of Quantum-Molecular Selection Processes Involved in the Evolution of Organisms and Species Previous theoretical or general approaches (Rosen, 1960; Shcherbik and Buchatsky, 2007) to the problems of Quantum Genetics and Molecular Evolution are considered in this article from the point of view of Quantum Automata Theory first published by the author in 1971 (Baianu,1971a, b) , and further developed in several recent articles (Baianu, 1977, 1983, 1987, 2004, 2011).The representation of genomes and Interactome networks in categories of many-valued logic LMn –algebras that are naturally transformed during biological evolution, or evolve through interactions with the environment provide a new insight into the mechanisms of molecular evolution, as well as organismal evolution, in terms of sequences of quantum automata. Phenotypic changes are expressed only when certain environmentally-induced quantum-molecular changes are coupled with an internal re-structuring of major submodules of the genome and Interactome networks related to cell cycling and cell growth. Contrary to the commonly held view of `standard’ Darwinist models of evolution, the evolution of organisms and species occurs through coupled multi-molecular transformations induced not only by the environment but actually realized through internal re-organizations of genome and interactome networks. The biological, evolutionary processes involve certain epigenetic transformations that are responsible for phenotypic expression of the genome and Interactome transformations initiated at the quantum-molecular level. It can thus be said that only quantum genetics can provide correct explanations of evolutionary processes that are initiated at the quantum—multi-molecular levels and propagate to the higher levels of organismal and species evolution. Biological evolution should be therefore regarded as a multi-scale process which is initiated by underlying quantum (coupled) multi-molecular transformations of the genomic and interactomic networks, followed by specific phenotypic transformations at the level of organism and the variable biogroupoids associated with the evolution of species which are essential to the survival of the species. The theoretical framework introduced in this article also paves the way to a Quantitative Biology approach to biological evolution at the quantum-molecular, as well as at the organismal and species levels. This is quite a substantial modification of the `established’ modern Darwinist, and also of several so-called `molecular evolution’ theories.Professor I.C. Baianu, ibaianu@illinois.edu2013-05-04T23:07:19Z2013-05-04T23:07:19Zhttp://cogprints.org/id/eprint/8908This item is in the repository with the URL: http://cogprints.org/id/eprint/89082013-05-04T23:07:19ZImproved Distributed Estimation Method for Environmental
time-variant Physical variables in Static Sensor NetworksIn this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably
and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm.Professor Magdi S. MahmoudDr. Haris M. Khalidharism.khalid@yahoo.comMr. Muhammad Sabih2011-12-16T00:58:48Z2011-12-16T00:58:48Zhttp://cogprints.org/id/eprint/7739This item is in the repository with the URL: http://cogprints.org/id/eprint/77392011-12-16T00:58:48ZNonlinear Models of Neural and Genetic Network Dynamics:
Natural Transformations of Łukasiewicz Logic LM-Algebras in a Łukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations
A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.
Professor I.C. Baianuibaianu@illinois.edu2011-08-30T04:15:08Z2011-08-30T04:15:08Zhttp://cogprints.org/id/eprint/7612This item is in the repository with the URL: http://cogprints.org/id/eprint/76122011-08-30T04:15:08ZArtífice: Generador de ciudades virtuales mediante autómatas celulares. Un enfoque desde los sistemas complejos y auto-organizantes.Existen sistemas que se componen de múltiples partes que interactúan de alguna forma para crear un comportamiento que no es derivable directamente de las características individuales de sus componentes, este tipo de sistemas se denominan sistemas complejos. Los sistemas complejos han sido estudiados con diversas técnicas, una de las más utilizadas han sido los autómatas celulares, debido a que son potentes motores conceptuales capaces de generar comportamientos complejos con reglas relativamente simples.
A lo largo de este trabajo nos enfocaremos en el estudio de un sistema complejo conocido por todos: la ciudad. Desde el siglo pasado ha sido de interés especial para los científicos el estudio del crecimiento de la ciudades porque éstas ha crecido notablemente -hasta un punto preocupante- en términos de terreno y pobladores.
Durante el desarrollo de la presente tesis se ha elaborado una herramienta computacional, llamada Artífice que nos permite analizar el crecimiento de ciudades ideales en términos de la ocupación de su terreno y su número de habitantes, además de lo anterior, Artífice brinda la posibilidad de crear una simulación en 3D de las ciudades hipotéticas que genera, utilizando un visor 3-Dimensional que ha sido optimizado mediante diversas técnicas de graficado por computadora para su mejor desempeño.Alexzander Arriaga Martinezalex.arriaga.m@gmail.comAbraham Sánchez Lópezasanchez@cs.buap.mxCarlos Gershensoncgg@unam.mxIsaac Rudomín Goldbergrudomin@itesm.mx2011-09-17T17:40:15Z2011-09-17T17:40:53Zhttp://cogprints.org/id/eprint/7373This item is in the repository with the URL: http://cogprints.org/id/eprint/73732011-09-17T17:40:15ZThe International Conference on Information and Communication Systems (ICICS 2011) he International Conference on Information and Communication Systems (ICICS 2011) is a forum for scientists, engineers, and practitioners to present their latest research results, ideas, developments, and applications in all areas of Computer and Information Sciences.Mr Mustafa Rdaidehmyradaideh@just.edu.jo2011-12-16T00:04:45Z2011-12-16T00:04:45Zhttp://cogprints.org/id/eprint/7736This item is in the repository with the URL: http://cogprints.org/id/eprint/77362011-12-16T00:04:45ZComplexity, Emergent Systems and Complex Biological Systems:
Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.
Prof. Dr I.C. Baianu2012-04-25T12:43:18Z2012-04-25T12:43:18Zhttp://cogprints.org/id/eprint/8199This item is in the repository with the URL: http://cogprints.org/id/eprint/81992012-04-25T12:43:18ZThe challenge of complexity for cognitive systemsComplex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research.Ute SchmidMarco RagniCleotilde GonzalezJoachim Funkefunke@uni-hd.de2012-11-25T12:35:22Z2013-02-18T15:10:43Zhttp://cogprints.org/id/eprint/8737This item is in the repository with the URL: http://cogprints.org/id/eprint/87372012-11-25T12:35:22ZComplex Neuro-Cognitive SystemsCognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology – nonlinear dynamical analysis – and to demonstrate its benefit
for cognitive neuroscience in cases when the usual reductionist method fails.Andreas Schierwagenschierwa@uni-leipzig.de2011-10-27T01:34:47Z2011-10-27T01:34:47Zhttp://cogprints.org/id/eprint/7663This item is in the repository with the URL: http://cogprints.org/id/eprint/76632011-10-27T01:34:47ZCrawling Facebook for Social Network Analysis PurposesWe describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.
Salvatore CatanesePasquale De MeoEmilio FerraraGiacomo FiumaraAlessandro Provetti2011-10-27T01:34:27Z2011-10-27T01:34:27Zhttp://cogprints.org/id/eprint/7668This item is in the repository with the URL: http://cogprints.org/id/eprint/76682011-10-27T01:34:27ZExtraction and Analysis of Facebook Friendship RelationsOnline Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms.Salvatore CatanesePasquale De MeoEmilio FerraraGiacomo FiumaraAlessandro Provetti2011-10-01T00:34:22Z2011-10-01T00:34:22Zhttp://cogprints.org/id/eprint/7634This item is in the repository with the URL: http://cogprints.org/id/eprint/76342011-10-01T00:34:22ZFinding Similar Users in FacebookOnline social networks are rapidly asserting themselves as popular services on the Web. A central point is
to determine whether two distinct users can be considered similar, a crucial concept with interesting consequences on the possibility to accomplish targeted actions like, for example, political and social aggregations or commercial promotions. In this chapter we propose an approach in order to estimate the similarity
of two users based on the knowledge of social ties (i.e., common friends and groups of users)
existing among users, and the analysis of activities (i.e., social events) in which users are involved. For
each of these indicators, we draw a local measure of user similarity which takes into account only their
joint behaviours. After this, we consider the whole network of relationships among users along with local
values of similarities and combine them to obtain a global measure of similarity. Such a computation is
carried out by applying the Katz coefficient, a popular parameter introduced in Social Science research.
Finally, similarity values produced for each social activity are merged into a unique value of similarity by
applying linear regression.Pasquale De MeoEmilio FerraraGiacomo Fiumara2011-10-27T01:34:32Z2011-10-27T01:34:32Zhttp://cogprints.org/id/eprint/7667This item is in the repository with the URL: http://cogprints.org/id/eprint/76672011-10-27T01:34:32ZGeneralized Louvain Method for Community Detection in Large NetworksIn this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the k-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.
Pasquale De MeoEmilio FerraraGiacomo FiumaraAlessandro Provetti2011-12-16T00:59:10Z2011-12-16T00:59:10Zhttp://cogprints.org/id/eprint/7757This item is in the repository with the URL: http://cogprints.org/id/eprint/77572011-12-16T00:59:10ZFrom Simple to Complex and Ultra-complex Systems:
A Paradigm Shift Towards Non-Abelian Systems DynamicsAtoms, molecules, organisms distinguish layers of reality because of the causal links that govern their behavior, both horizontally (atom-atom, molecule-molecule, organism-organism) and vertically (atom-molecule-organism). This is the first intuition of the theory of levels. Even if the further development of the theory will require imposing a number of qualifications to this initial intuition, the idea of a series of entities organized on different levels of complexity will prove correct. Living systems as well as social systems and the human mind present features remarkably different from those characterizing non-living, simple physical and chemical systems. We propose that super-complexity requires at least four different categorical frameworks, provided by the theories of levels of reality, chronotopoids, (generalized) interactions, and anticipation. Prof.Dr. I.C, BaianuicbProf.Dr. Roberto Poli2010-09-13T03:50:31Z2011-03-11T08:57:44Zhttp://cogprints.org/id/eprint/7019This item is in the repository with the URL: http://cogprints.org/id/eprint/70192010-09-13T03:50:31ZBorobudur was Built AlgorithmicallyThe self-similarity of Indonesian Borobudur Temple is observed through the dimensionality of stupa that is hypothetically closely related to whole architectural body. Fractal dimension is calculated by using the cube counting method and found that the dimension is 2.325, which is laid between the two-dimensional plane and three dimensional space. The applied fractal geometry and self-similarity of the building is emerged as the building process implement the metric rules, since there is no universal metric standard known in ancient traditional Javanese culture thus the architecture is not based on final master plan. The paper also proposes how the hypothetical algorithmic architecture might be applied computationally in order to see some experimental generations of similar building. The paper ends with some conjectures for further challenge and insights related to fractal geometry in Javanese traditional cultural heritages. Hokky Situngkir2010-09-13T03:57:35Z2011-03-11T08:57:40Zhttp://cogprints.org/id/eprint/6948This item is in the repository with the URL: http://cogprints.org/id/eprint/69482010-09-13T03:57:35ZFrom Domains Towards a Logic of Universals: A Small Calculus for the Continuous Determination of WorldsAt the end of the 19th century, 'logic' moved from the discipline of philosophy to that of mathematics. One hundred years later, we have a plethora of formal logics. Looking at the situation form informatics, the mathematical discipline proved only a temporary shelter for `logic'. For there is Domain Theory, a constructive mathematical theory which extends the notion of computability into the continuum and spans the field of all possible deductive systems. Domain Theory describes the space of data-types which computers can ideally compute -- and computation in terms of these types. Domain Theory is constructive but only potentially operational. Here one particular operational model is derived from Domain Theory which consists of `universals', that is, model independent operands and operators. With these universals, Domains (logical models) can be approximated and continuously determined. The universal data-types and rules derived from Domain Theory relate strongly to the first formal logic conceived on philosophical grounds, Aristotelian (categorical) logic. This is no accident. For Aristotle, deduction was type-dependent and he too thought in term of type independent universal `essences'. This paper initiates the next `logical' step `beyond' Domain Theory by reconnecting `formal logic' with its origin.Dr. Claus Brillowskibrillowski@logike.info2010-08-06T11:18:44Z2011-03-11T08:57:39Zhttp://cogprints.org/id/eprint/6904This item is in the repository with the URL: http://cogprints.org/id/eprint/69042010-08-06T11:18:44ZEvolution of Consumers’ Preferences due to InnovationThe integration process between evolutionary approach and conventional economic analysis is very essential for the next development of economic studies, especially in the fundamental concepts of modern economics: supply and demand analysis. In this presentation, we use the concept of meme to explore evolution of demand. This study offers an evolutionary model of demand, which views utility as a function of the distance between the two types of sequences of memes (memeplex), which represent economic product and consumer preference. It is very different from the conventional approach of demand, which only views utility as a function of quantity. This modification provides an opportunity to see innovation and transformation of consumer preferences in the demand perspective. Innovation is seen as a change in sequence of memes in economic products, while the transformation of consumer behavior is defined as a change in the aligning memes of consumer preference. Demand quantity is the result of the selection process. This model produces some interesting characteristics, such as: (i) quantitative and qualitative properties of evolution of demand, (ii) relationship between consumer behavior and properties of evolution of demand that occurred and (iii) power law on the distribution of product lifetime. At the end we show the improvement of utility function, in the concept of meme, might create a new landscape for the further development of economics.Rolan Mauludyrmd@compsoc.bandungfe.netHokky Situngkirhs@compsoc.bandungfe.net2011-12-16T00:04:40Z2011-12-16T00:04:40Zhttp://cogprints.org/id/eprint/7754This item is in the repository with the URL: http://cogprints.org/id/eprint/77542011-12-16T00:04:40ZCategorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.
KEYWORDS: Emergence of Life and Human Consciousness;
Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments.
Prof. Dr. I.C. Baianuibaianu@illinois.eduProf. Dr. James F. Glazebrookjfglazebrook@eiu.edu2011-12-16T00:58:12Z2011-12-16T00:58:12Zhttp://cogprints.org/id/eprint/7751This item is in the repository with the URL: http://cogprints.org/id/eprint/77512011-12-16T00:58:12ZŁukasiewicz-Moisil Many-Valued Logic Algebra of Highly-Complex SystemsA novel approach to self-organizing, highly-complex systems (HCS), such as living organisms and artificial intelligent systems (AIs), is presented which is relevant to Cognition, Medical Bioinformatics and Computational Neuroscience. Quantum Automata (QAs) were defined in our previous work as generalized, probabilistic automata with quantum state spaces (Baianu, 1971). Their next-state functions operate through transitions between quantum states defined by the quantum equations of motion in the Schroedinger representation, with both initial and boundary conditions in space-time. Such quantum automata operate with a quantum logic, or Q-logic, significantly different from either Boolean or Łukasiewicz many-valued logic. A new theorem is proposed which states that the category of quantum automata and automata--homomorphisms has both limits and colimits. Therefore, both categories of quantum automata and classical automata (sequential machines) are bicomplete. A second new theorem establishes that the standard automata category is a subcategory of the quantum automata category. The quantum automata category has a faithful representation in the category of Generalized (M,R)--Systems which are open, dynamic biosystem networks with defined biological relations that represent physiological functions of primordial organisms, single cells and higher organisms.Professor I.C. Baianuibaianu@illinois.eduProfessor George Georgescugeorgescu@funinf.cs.unibuc.roProfessor James F. Glazebrookjfglazebrook@eiu.edu2011-12-16T00:59:06Z2011-12-16T00:59:06Zhttp://cogprints.org/id/eprint/7756This item is in the repository with the URL: http://cogprints.org/id/eprint/77562011-12-16T00:59:06ZOncogenomics and Cancer InteractomicsAn overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out.Prof. Dr I.C. Baianuibaianu@illinois.edu2010-06-06T14:34:19Z2011-03-11T08:57:37Zhttp://cogprints.org/id/eprint/6852This item is in the repository with the URL: http://cogprints.org/id/eprint/68522010-06-06T14:34:19ZLandscape in the Economy of Conspicuous Consumptions
Psychological states side by side with the bounded rational expectations among social agents contributes to the pattern of consumptions in economic system. One of the psychological states are the envy – a tendency to emulate any gaps with other agents’ properties. The evolutionary game theoretic works on conspicuous consumption are explored by growing the micro-view of economic agency in lattice-based populations, the landscape of consumptions. The emerged macro-view of multiple equilibria is shown in computational simulative demonstrations altogether with the spatial clustered agents based upon the emerged agents’ economic profiles. Hokky Situngkirhs@compsoc.bandungfe.net2011-10-01T00:34:10Z2011-10-01T00:34:10Zhttp://cogprints.org/id/eprint/7641This item is in the repository with the URL: http://cogprints.org/id/eprint/76412011-10-01T00:34:10ZAnalyzing the Facebook Friendship GraphOnline Social Networks (OSN) during last years acquired a
huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re
ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities
between the developing of OSN and real-life social networks.Salvatore CatanesePasquale De MeoEmilio FerraraGiacomo Fiumara2009-11-14T11:28:27Z2011-03-11T08:57:33Zhttp://cogprints.org/id/eprint/6723This item is in the repository with the URL: http://cogprints.org/id/eprint/67232009-11-14T11:28:27ZMulti-Agent System Interaction in Integrated SCM
Coordination between organizations on strategic, tactical and operation levels leads to more effective and efficient supply chains. Supply chain management is increasing day by day in modern enterprises.. The environment is becoming competitive and many enterprises will find it difficult to survive if they do not make their sourcing, production and distribution more efficient. Multi-agent supply chain management has recognized as an effective methodology for supply chain management. Multi-agent systems (MAS) offer new methods compared to conventional, centrally organized architectures in the scope of supply chain management (SCM). Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this study we show how MAS architecture interacts in the integrated SCM architecture with the help of various intelligent agents to highlight the above problem.Ritu SindhuAbdul WahidG. N. Purohit2009-11-14T11:28:54Z2011-03-11T08:57:33Zhttp://cogprints.org/id/eprint/6721This item is in the repository with the URL: http://cogprints.org/id/eprint/67212009-11-14T11:28:54ZTechnology Integration around the Geographic Information: A State of the ArtOne of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented.Rafael Ponce-MedellinGabriel Gonzalez-SernaRocio VargasLirio Ruiz2009-10-15T22:58:04Z2011-03-11T08:57:26Zhttp://cogprints.org/id/eprint/6637This item is in the repository with the URL: http://cogprints.org/id/eprint/66372009-10-15T22:58:04ZCreativity as Cognitive design
The case of mesoscopic variables in Meta-Structures
Creativity is an open problem which has been differently approached by several disciplines since a long time. In this contribution we consider as creative the constructivist design an observer does on the description levels of complex phenomena, such as the self-organized and emergent ones ( e.g., Bènard rollers, Belousov-Zhabotinsky reactions, flocks, swarms, and more radical cognitive and social emergences). We consider this design as related to the Gestaltian creation of a language fit for representing natural processes and the observer in an integrated way. Organised systems, both artificial and most of the natural ones are designed/ modelled according to a logical closed model which masters all the inter-relation between their constitutive elements, and which can be described by an algorithm or a single formal model. We will show there that logical openness and DYSAM (Dynamical Usage of Models) are the proper tools for those phenomena which cannot be described by algorithms or by a single formal model. The strong correlation between emergence and creativity suggests that an open model is the best way to provide a formal definition of creativity. A specific application relates to the possibility to shape the emergence of Collective Behaviours. Different modelling approaches have been introduced, based on symbolic as well as sub-symbolic rules of interaction to simulate collective phenomena by means of computational emergence. Another approach is based on modelling collective phenomena as sequences of Multiple Systems established by percentages of conceptually interchangeable agents taking on the same roles at different times and different roles at the same time. In the Meta-Structures project we propose to use mesoscopic variables as creative design, invention, good continuity and imitation of the description level. In the project we propose to define the coherence of sequences of Multiple Systems by using the values taken on by the dynamic mesoscopic clusters of its constitutive elements, such as the instantaneous number of elements having, in a flock, the same speed, distance from their nearest neighbours, direction and altitude. In Meta-Structures the collective behaviour’s coherence corresponds, for instance, to the scalar values taken by speed, distance, direction and altitude along time, through statistical strategies of interpolation, quasi-periodicity, levels of ergodicity and their reciprocal relationship. In this case the constructivist role of the observer is considered creative as it relates to neither non-linear replication nor transposition of levels of description and models used for artificial systems, like reductionism. Creativity rather lies in inventing new mesoscopic variables able to identify coherent patterns in complex systems. As it is known, mesoscopic variables represent partial macroscopic properties of a system by using some of the microscopic degrees of freedom possessed by composing elements. Such partial usage of microscopic as well as macroscopic properties allows a kind of Gestaltian continuity and imitation between levels of descriptions for mesoscopic modelling.
Prof. Ignazio Licataignazio.licata@ejtp.infp Prof. Gianfranco Minatigianfranco.minati@airs.it2009-11-14T11:32:24Z2011-03-11T08:57:32Zhttp://cogprints.org/id/eprint/6709This item is in the repository with the URL: http://cogprints.org/id/eprint/67092009-11-14T11:32:24ZContext-aware Authorization in Highly Dynamic EnvironmentsHighly dynamic computing environments, like ubiquitous and pervasive computing environments, require frequent adaptation of applications. Context is a key to adapt suiting user needs. On the other hand, standard access control trusts users once they have authenticated, despite the fact that they may reach unauthorized contexts. We analyse how taking into account dynamic information like context in the authorization subsystem can improve security, and how this new access control applies to interaction patterns, like messaging or eventing. We experiment and validate our approach using context as an authorization factor for eventing in Web service for device (like UPnP or DPWS), in smart home security.Jean-Yves TIGLIStephane LAVIROTTEGaetan REYVincent HourdinMichel RIVEILL2009-11-14T11:30:38Z2011-03-11T08:57:33Zhttp://cogprints.org/id/eprint/6716This item is in the repository with the URL: http://cogprints.org/id/eprint/67162009-11-14T11:30:38ZDAMQ-Based Schemes for chemes Efficiently Using the Buffer Spaces of a NoC RouterIn this paper we present high performance dynamically allocated multi-queue (DAMQ) buffer schemes for fault tolerance systems on chip applications that require an interconnection network. Two or four virtual channels shared the same buffer space. On the message switching layer, we make improvement to boost system performance when there are faults involved in the components communication. The proposed schemes are when a node or a physical channel is deemed as faulty, the previous hop node will terminate the buffer occupancy of messages destined to the failed link. The buffer usage decisions are made at switching layer without interactions with higher abstract layer, thus buffer space will be released to messages destined to other healthy nodes quickly. Therefore, the buffer space will be efficiently used in case fault occurs at some nodes.
Keywords: Network on chip, Fault tolerance, DAMQS, DAMQAS, Buffer space, Odd-even routing algorithmMohammad Ali Jabraeil JamaliAhmad Khademzadeh2009-09-07T10:18:17Z2011-03-11T08:57:24Zhttp://cogprints.org/id/eprint/6609This item is in the repository with the URL: http://cogprints.org/id/eprint/66092009-09-07T10:18:17ZThe thermodynamics of human reaction timesI present a new approach for the interpretation of reaction time (RT) data from behavioral experiments. From a physical perspective, the entropy of the RT distribution provides a model-free estimate of the amount of processing performed by the cognitive system. In this way, the focus is shifted from the conventional interpretation of individual RTs being either long or short, into their distribution being
more or less complex in terms of entropy. The new approach enables the estimation of the cognitive processing load without reference to the informational content of the stimuli themselves, thus providing a more appropriate estimate of the cognitive impact of dierent sources of information that are carried by experimental stimuli or tasks. The paper introduces the formulation of the theory, followed by an empirical validation using a database of human RTs in lexical tasks (visual lexical decision and word
naming). The results show that this new interpretation of RTs is more powerful than the traditional one. The method provides theoretical estimates of the processing loads elicited by individual stimuli. These loads sharply distinguish the responses from different tasks. In addition, it provides upper-bound estimates for the speed at which the system processes information. Finally, I argue that the theoretical proposal, and the associated empirical evidence, provide strong arguments for an adaptive system that systematically adjusts its operational processing speed to the particular demands of each stimulus. This
finding is in contradiction with Hick's law, which posits a relatively constant processing speed within an experimental context.Dr Fermin Moscoso del Prado Martinfermosc@gmail.com2009-11-14T11:35:23Z2011-03-11T08:57:31Zhttp://cogprints.org/id/eprint/6693This item is in the repository with the URL: http://cogprints.org/id/eprint/66932009-11-14T11:35:23ZComprehensive Security Framework for Global Threats AnalysisCyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios.Jacques SaraydaryanFatiha BenaliStéphane Ubeda2009-11-14T11:34:51Z2011-03-11T08:57:31Zhttp://cogprints.org/id/eprint/6697This item is in the repository with the URL: http://cogprints.org/id/eprint/66972009-11-14T11:34:51ZDPRAODV: A Dynamic Learning System Against Blackhole Attack In AODV Based MANETSecurity is an essential requirement in mobile ad hoc networks to provide protected communication between mobile nodes. Due to unique characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a multifence security solution that achieves both broad protection and desirable network performance. MANETs are vulnerable to various attacks, blackhole, is one of the possible attacks. Black hole is a type of routing attack where a malicious node advertise itself as having the shortest path to all nodes in the environment by sending fake route reply. By doing this, the malicious node can deprive the traffic from the source node. It can be used as a denial-of-service attack where it can drop the packets later. In this paper, we proposed a DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver-2.33) demonstrate that our protocol not only prevents blackhole attack but consequently improves the overall performance of (normal) AODV in presence of black hole attack.Payal N. RajPrashant B. Swadas2009-11-14T11:32:45Z2011-03-11T08:57:32Zhttp://cogprints.org/id/eprint/6704This item is in the repository with the URL: http://cogprints.org/id/eprint/67042009-11-14T11:32:45ZGeometric and Signal Strength Dilution of Precision (DoP) Wi-FiThe democratization of wireless networks combined to the emergence of mobile devices increasingly autonomous and efficient lead to new services. Positioning services become overcrowded. Accuracy is the main quality criteria in positioning. But to better appreciate this one a coefficient is needed. In this paper we present Geometric and Signal Strength Dilution of Precision (DOP) for positioning systems based on Wi-Fi and Signal Strength measurements.Soumaya ZirariPhilippe CanaldaFrançois Spies2009-11-14T11:35:30Z2011-03-11T08:57:31Zhttp://cogprints.org/id/eprint/6692This item is in the repository with the URL: http://cogprints.org/id/eprint/66922009-11-14T11:35:30ZGlobal Heuristic Search on Encrypted Data (GHSED)Important document are being kept encrypted in remote servers. In order to retrieve these encrypted data, efficient search methods needed to enable the retrieval of the document without knowing the content of the documents In this paper a technique called a global heuristic search on encrypted data (GHSED) technique will be described for search in an encrypted files using public key encryption stored on an untrusted server and retrieve the files that satisfy a certain search pattern without revealing any information about the original files. GHSED technique would satisfy the following: (1) Provably secure, the untrusted server cannot learn anything about the plaintext given only the cipher text. (2) Provide controlled searching, so that the untrusted server cannot search for a word without the user's authorization. (3) Support hidden queries, so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. (4) Support query isolation, so the untrusted server learns nothing more than the search result about the plaintext.Maisa HalloushMai Sharif2009-11-14T11:35:36Z2011-03-11T08:57:31Zhttp://cogprints.org/id/eprint/6691This item is in the repository with the URL: http://cogprints.org/id/eprint/66912009-11-14T11:35:36ZPhilosophical Survey of PasswordsOver the years security experts in the field of Information Technology have had a tough time in making passwords secure. This paper studies and takes a careful look at this issue from the angle of philosophy and cognitive science. We have studied the process of passwords to rank its strengths and weaknesses in order to establish a quality metric for passwords. Finally we related the process to human senses which enables us to propose a constitutional scheme for the process of password. The basic proposition is to exploit relationship between human senses and password to ensure improvement in authentication while keeping it an enjoyable activity.M. Atif QureshiArjumand YounusArslan Ahmed Khan2009-11-14T11:33:08Z2011-03-11T08:57:32Zhttp://cogprints.org/id/eprint/6701This item is in the repository with the URL: http://cogprints.org/id/eprint/67012009-11-14T11:33:08ZSimilarity Matching Techniques For Fault Diagnosis In Automotive Infotainment ElectronicsFault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed.Mashud Kabir2009-11-14T11:35:42Z2011-03-11T08:57:31Zhttp://cogprints.org/id/eprint/6690This item is in the repository with the URL: http://cogprints.org/id/eprint/66902009-11-14T11:35:42ZTowards a General Definition of Biometric SystemsA foundation for closing the gap between biometrics in the narrower and the broader perspective is presented trough a conceptualization of biometric systems in both perspectives. A clear distinction between verification, identification and classification systems is made as well as shown that there are additional classes of biometric systems. In the end a Unified Modeling Language model is developed showing the connections between the two perspectives.Markus SchattenMiroslav BacaMirko Cubrilo2009-07-06T09:43:03Z2011-03-11T08:57:23Zhttp://cogprints.org/id/eprint/6577This item is in the repository with the URL: http://cogprints.org/id/eprint/65772009-07-06T09:43:03ZRobustness and Adaptability Analysis of Future Military Air Transportation FleetsMaking decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios.Dr Slawomir WesolkowskiMr Michael MazurekDr James M Whitacrejwhitacre79@yahoo.comDr Hussein AbbassDr Axel Bender2009-07-02T01:50:34Z2011-03-11T08:57:22Zhttp://cogprints.org/id/eprint/6555This item is in the repository with the URL: http://cogprints.org/id/eprint/65552009-07-02T01:50:34ZThreshold Verification Technique for Network Intrusion Detection SystemInternet has played a vital role in this modern world, the possibilities and opportunities offered are limitless. Despite all the hype, Internet services are liable to intrusion attack that could tamper the confidentiality and integrity of important information. An attack started with gathering the information of the attack target, this gathering of information activity can be done as either fast or slow attack. The defensive measure network administrator can take to overcome this liability is by introducing Intrusion Detection Systems (IDSs) in their network. IDS have the capabilities to analyze the network traffic and recognize incoming and on-going intrusion. Unfortunately the combination of both modules in real time network traffic slowed down the detection process. In real time network, early detection of fast attack can prevent any further attack and reduce the unauthorized access on the targeted machine. The suitable set of feature selection and the correct threshold value, add an extra advantage for IDS to detect anomalies in the network. Therefore this paper discusses a new technique for selecting static threshold value from a minimum standard features in detecting fast attack from the victim perspective. In order to increase the confidence of the threshold value the result is verified using Statistical Process Control (SPC). The implementation of this approach shows that the threshold selected is suitable for identifying the fast attack in real time.A. Mohd Faizal M. Mohd Zaki S. Shahrin Y. Robiah S. Siti Rahayu 2009-07-02T01:51:39Z2011-03-11T08:57:22Zhttp://cogprints.org/id/eprint/6551This item is in the repository with the URL: http://cogprints.org/id/eprint/65512009-07-02T01:51:39ZComplexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity
This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.
Mr. Debaprasad Mukherjeebiodeb@gmail.com2010-01-30T03:40:56Z2011-03-11T08:57:35Zhttp://cogprints.org/id/eprint/6769This item is in the repository with the URL: http://cogprints.org/id/eprint/67692010-01-30T03:40:56ZHow Creative Should Creators be to Optimize the Evolution of Ideas? A Computer ModelThere are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper outlines investigations of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. The ideal proportion with respect to fitness of ideas is found to depend on the level of creativity of the creative agents. For all levels or creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents.Stefan Leijnenstefanleijnen@gmail.comDr. Liane Gaboraliane.gabora@ubc.ca2008-12-17T22:13:26Z2011-03-11T08:57:17Zhttp://cogprints.org/id/eprint/6295This item is in the repository with the URL: http://cogprints.org/id/eprint/62952008-12-17T22:13:26ZDeconstructing Javanese Batik Motif: When Traditional Heritage Meets ComputationThe paper discusses some aspects of Iterated Function System while referring to some interesting point of view into Indonesian traditional batik. The deconstruction is delivered in our recognition of the Collage Theorem to find the affine transform of the iterated function system that attracts the iteration of drawing the dots into the complex motif of – or at least, having high similarity to – batik patterns. We employ and revisit the well-known Chaos Game to reconstruct after having some basic motifs is deconstructed. The reconstruction of the complex pattern opens a quest of creativity broadening the computationally generated batik exploiting its self-similarity properties. A challenge to meet the modern computational generative art with the traditional batik designs is expected to yield synergistically interesting results aesthetically. The paper concludes with two arrows of our further endeavors in this field, be it enriching our understanding of how human cognition has created such beautiful patterns and designs traditionally since ancient civilizations in our anthropological perspective while in the other hand providing us tool to the empowerment of batik as generative aesthetics by employment of computation.
Hokky Situngkirhs@compsoc.bandungfe.net2009-03-04T03:19:37Z2011-03-11T08:57:19Zhttp://cogprints.org/id/eprint/6370This item is in the repository with the URL: http://cogprints.org/id/eprint/63702009-03-04T03:19:37ZConsiderations on Resource Usage in Exceptions and Failures in WorkflowsThe paper presents a description of some point of view of different authors related to the failures and exceptions that appear in workflows, as a direct consequence of unavailability of resources involved in the workflow.
Each of these interpretations is typical for a certain situation, depending on the authors' interpretation of failures and exceptions in workflows modeling real dynamical systems.
Alexandra Fortisafortis@tibiscus.roAlexandru CicortasVictoria Iordan2008-10-16T13:47:59Z2011-03-11T08:57:12Zhttp://cogprints.org/id/eprint/6222This item is in the repository with the URL: http://cogprints.org/id/eprint/62222008-10-16T13:47:59ZEvolutionary Economics celebrates Innovation and Creativity based Economy
The paper draws issue on the evolutionary economics that open our mind on seeing economy as growing and living organism with any characters of robustness, self-organization, adaptation, and evolution. This has been recognized, as in global picture, we enter the phase in which information and knowledge acquisition rapidly plays a major role in economy. The discussions is presented by demonstrating some qualitative properties and theoretical explorations on long range historical economic growth and development and thus followed by some highlights on innovation, creativity and elaborations regarding to fitness landscapes incorporating memetics, as works related to social and cultural aspects of social system, while talking about economic system in general. The discussions depicts some important notions on market and product diversifications that have been the source of the economic growth in general. Hokky Situngkirhs@compsoc.bandungfe.net2009-10-15T22:58:25Z2011-03-11T08:57:27Zhttp://cogprints.org/id/eprint/6638This item is in the repository with the URL: http://cogprints.org/id/eprint/66382009-10-15T22:58:25ZLogical openness in Cognitive Models It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of logical openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe cognition by means of semantics which fix the system/environment relationship (cognition in vitro), while the sub-symbolic ones with high logical openness tends to seize its evolutive dynamics (cognition in vivo). An observer is defined as a system with high logical openness. In conclusion, the characteristic processes of intrinsic emergence typical of “bio-logic” - emerging of new codes-require an alternative model to Turing-computation, the natural or bio-morphic computation, whose essential features we are going here to outline.Prof. Ignazio Licataignazio.licata@ejtp.info2008-02-25T22:48:32Z2011-03-11T08:57:04Zhttp://cogprints.org/id/eprint/5941This item is in the repository with the URL: http://cogprints.org/id/eprint/59412008-02-25T22:48:32ZPrinciples for Consciousness in Integrated Cognitive ControlIn this article we will argue that given certain conditions for the evolution of bi-
ological controllers, these will necessarily evolve in the direction of incorporating
consciousness capabilities. We will also see what are the necessary mechanics for
the provision of these capabilities and extrapolate this vision to the world of artifi-
cial systems postulating seven design principles for conscious systems. This article
was published in the journal Neural Networks special issue on brain and conscious-
ness.Ricardo SanzRicardo.Sanz@etsii.upm.esIgnacio LopezIgnacio.Lopez@etsii.upm.esManuel RodriguezManuel.Rodriguez@etsii.upm.esCarlos HernandezCarlso.Hernandez@etsii.upm.es2008-10-22T01:17:40Z2011-03-11T08:57:13Zhttp://cogprints.org/id/eprint/6237This item is in the repository with the URL: http://cogprints.org/id/eprint/62372008-10-22T01:17:40ZEvolution of Prehension Ability in an Anthropomorphic Neurorobotic ArmIn this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.Prof Angelo Cangelosiacangelosi@plymouth.ac.ukGianluca MasseraStefano Nolfi2007-08-20Z2011-03-11T08:56:57Zhttp://cogprints.org/id/eprint/5670This item is in the repository with the URL: http://cogprints.org/id/eprint/56702007-08-20ZTowards Complexity Studies of Indonesian Songs
We see some complex properties from Indonesian music discography by means of music as perceived by Indonesian people. This covers the folk songs, national anthems, popular songs by Indonesian modern artists and performers and also from western popular and classical music as reference. The self-similarity is drawn by using the model of gyration and the internal dynamics of the pitches and durations used in songs is observed by using the logarithmic spiral model. The employed entropy model is also discussed as well as introduction to the calculated dynamic complexity of melodic structure. Some generalization on the flow of music respect to the dynamic complexity is also shown. We discover that at least there are two phases in the played song: the shorter introductory phase that ends in the peak of complexity of the song and the attenuating phase of complexity in which the multiple equilibria of the song is measured. The paper draws some interesting aspects regarding to those parameters and variables on Indonesian melodic corpora. Hokky Situngkir2007-07-28Z2011-03-11T08:56:55Zhttp://cogprints.org/id/eprint/5621This item is in the repository with the URL: http://cogprints.org/id/eprint/56212007-07-28ZSmall World Network of Athletes: Graph Representation of the World Professional Tennis Player
The paper proposes an alternative way to observe and extract the multiple matches games of sports, i.e.: tennis tournament in the Athlete’s Historical Relative Performance Index and its representation as graph. The finding of the small world topology is elaborated along with further statistical patterns in the fashion of the weighted and directed network. The explanation of the sport tournament system as a highly optimized system is hypothetically proposed. Finally, some elaborations regarding to further directions of the usability of the proposed methodology is discussed.Hokky Situngkir2009-03-28T09:29:33Z2011-03-11T08:57:20Zhttp://cogprints.org/id/eprint/6402This item is in the repository with the URL: http://cogprints.org/id/eprint/64022009-03-28T09:29:33ZAt the Potter’s Wheel: An Argument for Material AgencyConsider a potter throwing a vessel on the wheel. Think of the complex ways brain, body, wheel and clay relate and interact with one another throughout the different stages of this activity and try to imagine some of the resources (physical, mental or biological) needed for the enaction of this creative process. Focus, for instance, on the first minutes of action when the potter attempts to centre the lump of clay on the wheel. The hands are grasping the clay. The fingers, bent slightly following the surface curvature, sense the clay and exchange vital tactile information necessary for a number of crucial decisions that are about to follow in the next few seconds. What is it that guides
the dextrous positioning of the potter’s hands and decides upon the precise amount of forward or downward pressure necessary for centring a lump of clay on the wheel? How do the potter’s fingers come to know the precise force of the
appropriate grip? What makes these questions even more fascinating is the ease by which the phenomena which they describe are accomplished. Yet underlying the effortless manner in which the potter’s hand reaches for and gradually
shapes the wet clay lies a whole set of conceptual challenges to some of our most deeply entrenched assumptions about what it means to be a human agent.Dr Lambros Malafouris2007-07-28Z2011-03-11T08:56:55Zhttp://cogprints.org/id/eprint/5620This item is in the repository with the URL: http://cogprints.org/id/eprint/56202007-07-28ZHistorical Relative Performance Index over Interconnectedness of Badminton AthletesThe paper proposes the Historical Relative Performance Index in order to quantitatively extract information in the scores hit in the sets of head-to-head game in badminton tournaments. The index is treated as the weights of the directed networks built between competing athletes. The paper also proposes the way to build the fully connected network based on the empirically found network in order to have relative index between athletes that have never nor will be met in series of games. Some further directions as well as implementation to small amount of data is described for advanced analysis. Hokky SitungkirDeni KhanafiahRolan Mauludy2007-10-22T10:40:41Z2011-03-11T08:56:59Zhttp://cogprints.org/id/eprint/5779This item is in the repository with the URL: http://cogprints.org/id/eprint/57792007-10-22T10:40:41ZAutonomy: a review and a reappraisalIn the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between behavioral and constitutive autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved.Mr Tom Froeset.froese@gmail.comMr Nathaniel Virgon.d.virgo@sussex.ac.ukMr Eduardo Izquierdoe.j.izquierdo@sussex.ac.uk2007-04-04Z2011-03-11T08:56:49Zhttp://cogprints.org/id/eprint/5473This item is in the repository with the URL: http://cogprints.org/id/eprint/54732007-04-04ZIntrinsic Motivation Systems for Autonomous Mental DevelopmentExploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values.
Pierre-Yves OudeyerFrédéric KaplanVéréna Hafner2006-12-03Z2011-03-11T08:56:42Zhttp://cogprints.org/id/eprint/5263This item is in the repository with the URL: http://cogprints.org/id/eprint/52632006-12-03ZAdvertising in Duopoly MarketThe paper presents the dynamics of consumer preferences over two competing products acting in duopoly market. The model presented compared the majority and minority rules as well as the modified Snazjd model in the Von Neumann neighborhood. We showed how important advertising in marketing a product is. We show that advertising should also consider the social structure simultaneously with the content of the advertisement and the understanding to the advertised product. Some theoretical explorations are discussed regarding to size of the market, evaluation of effect of the advertising, the types of the advertised products, and the social structure of which the product is marketed. We also draw some illustrative models to be mproved as a further work.Hokky Situngkir2006-12-08Z2011-03-11T08:56:43Zhttp://cogprints.org/id/eprint/5275This item is in the repository with the URL: http://cogprints.org/id/eprint/52752006-12-08ZBrain Dynamics across levels of OrganizationAfter presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the Dynamic Core (DC) and the Global Neuronal Workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene-Changeux, on the other. In both cases, the recursively reentrant activity flow in intra-cortical and cortical-subcortical neuron loops plays an essential and distinct role. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of Self-Organized Criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical Physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG.M.D. Gerhard Werner2006-10-23Z2011-03-11T08:56:39Zhttp://cogprints.org/id/eprint/5229This item is in the repository with the URL: http://cogprints.org/id/eprint/52292006-10-23ZA realistic simulation for self-organizing traffic lightsTraffic density has been growing during the last decades. New and better
traffic light controllers are needed. Carlos Gershenson has proposed self-
organizing traffic light controllers which are much better than current "green wave" methods.
This has been tested by simulation with a realistic traffic simulator, which is an extended version of the Green Light District
/ iAtracos project. The simulations of the traffic light controllers are done
for three scenarios. The third scenario is the Wetstraat of Brussels, which
is created to approach the real infrastructure and traffic dynamics. The
simulation results show that the proposed Sotl-platoon controller is much
better than the green wave controller.Seung Bae Cools2006-08-01Z2011-03-11T08:56:33Zhttp://cogprints.org/id/eprint/5038This item is in the repository with the URL: http://cogprints.org/id/eprint/50382006-08-01ZSelf-Replication and Self-Assembly for ManufacturingIt has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach
to very low-cost manufacturing. The research presented here is intended to
be a step towards this vision. We describe a computational simulation of
nanoscale machines floating in a virtual liquid. The machines can bond
together to form strands (chains) that self-replicate and self-assemble
into user-specified meshes. There are four types of machines and the
sequence of machine types in a strand determines the shape of the mesh
they will build. A strand may be in an unfolded state, in which the bonds
are straight, or in a folded state, in which the bond angles depend on the
types of machines. By choosing the sequence of machine types in a strand,
the user can specify a variety of polygonal shapes. A simulation typically
begins with an initial unfolded seed strand in a soup of unbonded machines.
The seed strand replicates by bonding with free machines in the soup. The
child strands fold into the encoded polygonal shape, and then the polygons
drift together and bond to form a mesh. We demonstrate that a variety of
polygonal meshes can be manufactured in the simulation, by simply changing
the sequence of machine types in the seed.Robert EwaschukPeter D. Turney21752006-05-25Z2011-03-11T08:56:25Zhttp://cogprints.org/id/eprint/4881This item is in the repository with the URL: http://cogprints.org/id/eprint/48812006-05-25ZModelling and control of chaotic processes
through their Bifurcation Diagrams generated
with the help of Recurrent Neural Networks
models Part 2 - Industrial StudyMany real-world processes tend to be chaotic and are not amenable to satisfactory
analytical models. It has been shown here that for such chaotic processes represented
through short chaotic noisy observed data, a multi-input and multi-output recurrent
neural network can be built which is capable of capturing the process trends and
predicting the behaviour for any given starting condition. It is further shown that
this capability can be achieved by the recurrent neural network model when it is
trained to very low value of mean squared error. Such a model can then be used
for constructing the Bifurcation Diagram of the process leading to determination
of desirable operating conditions. Further, this multi-input and multi-output model
makes the process accessible for control using open-loop / closed-loop approaches
or bifurcation control etc.Krishnaiah JalluC.S. KumarM.A. Faruqi2006-04-21Z2011-03-11T08:56:23Zhttp://cogprints.org/id/eprint/4842This item is in the repository with the URL: http://cogprints.org/id/eprint/48422006-04-21ZModelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes.
Krishnaiah Jalluj.krishnaiah@gmail.comS Kumar CM Aslam Faruqi2006-05-25Z2011-03-11T08:56:25Zhttp://cogprints.org/id/eprint/4883This item is in the repository with the URL: http://cogprints.org/id/eprint/48832006-05-25ZModelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes.
Krishnaiah Jj.krishnaiah@gmail.comS Kumar CM Aslam Faruqi2006-01-14Z2011-03-11T08:56:19Zhttp://cogprints.org/id/eprint/4701This item is in the repository with the URL: http://cogprints.org/id/eprint/47012006-01-14ZProof of the deadlock-freeness of ALD routing algorithmThis is the appendix to the paper Load-Balanced Adaptive Routing for Torus Networks to provide a detailed, formal proof of the deadlock-freeness of the routing algorithm proposed in the paper. The paper is submitted to Electronics Letters, and the abstract of which is as follows:
A new routing algorithm for torus interconnection networks to achieve high throughput on various traffic patterns, Adaptive Load-balanced routing with cycle Detection (ALD), is presented. Instead of the -channels scheme adopted in a few recently proposed algorithms of the same category, a cycle detection scheme is employed in ALD to handle deadlock, which leads to higher routing adaptability. Simulation results demonstrate that ALD achieves higher throughput than the recently proposed algorithms on both benign and adversarial traffic patterns. Hong WangDu XuGuo JiangYao YaoShizhong XuLemin Li2006-09-08Z2011-03-11T08:56:35Zhttp://cogprints.org/id/eprint/5129This item is in the repository with the URL: http://cogprints.org/id/eprint/51292006-09-08ZMetastability, Criticality and Phase Transitions in brain and its ModelsThis essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures.MD Gerhard Werner2006-10-05Z2011-03-11T08:56:38Zhttp://cogprints.org/id/eprint/5204This item is in the repository with the URL: http://cogprints.org/id/eprint/52042006-10-05ZStrategic SNA prospectSNA has been applied until now by computerized means to communities pertaining to the enterprise; we suggest to extend it to partners outside the enterprise and unify the results to the whole game of actors so that a global strategy could be worked up in a dynamic way. G. Benchimol2006-01-06Z2011-03-11T08:56:18Zhttp://cogprints.org/id/eprint/4677This item is in the repository with the URL: http://cogprints.org/id/eprint/46772006-01-06ZHerding to A Side of Order Book BalanceIn the growing econophysics, it is quite rare that the presented analyses approach the interesting properties of order book. However, a lot of data are available in the order book, and analysis on this object will bring us to further understanding of the market. We analyze some order book of stocks traded in Jakarta Stock Exchange and see interesting properties there by looking at the balance of the order book. We also build a model based on the work of Chiarella and Iori [1] with some modi¯cations to explain the herding pattern of traders through the order book.Hokky SitungkirYohanes Surya2006-01-06Z2011-03-11T08:56:18Zhttp://cogprints.org/id/eprint/4678This item is in the repository with the URL: http://cogprints.org/id/eprint/46782006-01-06ZTheorizing CorruptionThe aim of this paper is to gain the broad explanation of corruption using simple computational model. We elaborated further the model of corruption described previously in Situngkir (2003b), with some additions in model’s properties. We performed hundreds of experiments computationally using Swarm and constructed the explanation of corruption based upon these results. We show that corruption should be understood as complex social-phenomena, which relates not only with economical aspect, but also with many other social and anthropological aspects. Deni KhanafiahHokky Situngkir2005-02-26Z2011-03-11T08:55:51Zhttp://cogprints.org/id/eprint/4110This item is in the repository with the URL: http://cogprints.org/id/eprint/41102005-02-26ZSelf-Replicating Strands that Self-Assemble into User-Specified MeshesIt has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach to
very low-cost manufacturing. The research presented here is intended to be a
step towards this vision. In previous work (JohnnyVon 1.0), we simulated
machines that bonded together to form self-replicating strands. There were two
types of machines (called types 0 and 1), which enabled strands to encode
arbitrary bit strings. However, the information encoded in the strands had no
functional role in the simulation. The information was replicated without being
interpreted, which was a significant limitation for potential manufacturing
applications. In the current work (JohnnyVon 2.0), the information in a strand
is interpreted as instructions for assembling a polygonal mesh. There are now
four types of machines and the information encoded in a strand determines how
it folds. A strand may be in an unfolded state, in which the bonds are straight
(although they flex slightly due to virtual forces acting on the machines), or
in a folded state, in which the bond angles depend on the types of machines. By
choosing the sequence of machine types in a strand, the user can specify a
variety of polygonal shapes. A simulation typically begins with an initial
unfolded seed strand in a soup of unbonded machines. The seed strand replicates
by bonding with free machines in the soup. The child strands fold into the
encoded polygonal shape, and then the polygons drift together and bond to form
a mesh. We demonstrate that a variety of polygonal meshes can be manufactured
in the simulation, by simply changing the sequence of machine types in the
seed.Robert EwaschukPeter Turney21752006-07-23Z2011-03-11T08:56:29Zhttp://cogprints.org/id/eprint/4969This item is in the repository with the URL: http://cogprints.org/id/eprint/49692006-07-23ZDevelopmental acquisition of entrainment skills in
robot swinging using van der Pol oscillatorsIn this study we investigated the effects of different
morphological configurations on a robot swinging
task using van der Pol oscillators. The task was
examined using two separate degrees of freedom
(DoF), both in the presence and absence of neural
entrainment. Neural entrainment stabilises the
system, reduces time-to-steady state and relaxes the
requirement for a strong coupling with the
environment in order to achieve mechanical
entrainment. It was found that staged release of the
distal DoF does not have any benefits over using both
DoF from the onset of the experimentation. On the
contrary, it is less efficient, both with respect to the
time needed to reach a stable oscillatory regime and
the maximum amplitude it can achieve. The same
neural architecture is successful in achieving
neuromechanical entrainment for a robotic walking
task.Paschalis VeskosYiannis Demiris2006-07-23Z2011-03-11T08:56:29Zhttp://cogprints.org/id/eprint/4974This item is in the repository with the URL: http://cogprints.org/id/eprint/49742006-07-23ZDynamical Systems Approach to Infant Motor
Development: Implications for Epigenetic RoboticsEugene C. Goldfield2006-07-23Z2011-03-11T08:56:31Zhttp://cogprints.org/id/eprint/4995This item is in the repository with the URL: http://cogprints.org/id/eprint/49952006-07-23ZA formal approach of developmental robotics and psychologyKen PrepinPhilippe GaussierArnaud RevelJacqueline Nadel2005-07-13Z2011-03-11T08:56:08Zhttp://cogprints.org/id/eprint/4471This item is in the repository with the URL: http://cogprints.org/id/eprint/44712005-07-13ZRealistic description of causality in truly complex hierarchical structuresIn his recent essay article entitled "Physics, Complexity and Causality" (Nature 435, 743; 2005) George Ellis states that despite well-known successes of physics "we still do not have a realistic description of causality in truly complex hierarchical structures". Whereas one can only support the author's view that such description is increasingly desirable, the main statement suffers from essential incompleteness, since a realistic, mathematically rigorous and universally applicable description of detailed cause-effect links in truly complex systems, including e.g. emergent consciousness dynamics, does exist and is easily accessible through internet sources. As these results have been successfully presented at many international conferences (and even published!), the true problem is not the absence of realistic complexity description, but its intentional neglect, without any scientific objection, by the same physics establishment that insists (rightly) upon the necessity of that "new kind of science". As today's science has started to strongly modify the whole depth of unreduced dynamic complexity at all its levels using purely empirical, blind technology, further intentional rejection of consistent understanding of the real world complexity inevitably leads to destructive, catastrophically growing consequences.Andrei Kirilyuk2005-10-20Z2011-03-11T08:56:11Zhttp://cogprints.org/id/eprint/4560This item is in the repository with the URL: http://cogprints.org/id/eprint/45602005-10-20ZTowards Sustainable Future by Transition to the Next Level CivilisationUniversal and rigorously derived concept of dynamic complexity shows that any system of interacting components, including society and civilisation, exists only as a process of highly inhomogeneous, qualitative development of its complexity. Modern state of civilisation corresponds to the end of unfolding of a big enough level of complexity. Such exhausted, totally "replete" structure cannot be sustainable in principle and shows instead increased instability, realising its inevitable replacement by a new kind of structure with either low or much higher level of complexity (degrading or progressive development branch, respectively). Unrestricted sustainability can emerge only after transition to the next, superior level of civilisation complexity, which implies qualitative and unified changes in all aspects of life, including knowledge, production, social organisation, and infrastructure (cogprints.org/4113). These changes are specified by the rigorous analysis of underlying interaction processes. The unitary, effectively one-dimensional and rigidly fixed kind of thinking, knowledge, and social structure at the current level of complexity will be replaced by "dynamically multivalued", intrinsically creative kind of structure at the forthcoming superior level of development and consciousness. We propose mathematically rigorous description of unreduced civilisation complexity development, including universal criterion of progress. One obtains thus a working basis for the causally complete, objectively exact and reliable development science and futurology.Andrei Kirilyuk2006-03-18Z2011-03-11T08:56:21Zhttp://cogprints.org/id/eprint/4785This item is in the repository with the URL: http://cogprints.org/id/eprint/47852006-03-18ZUnreduced Dynamic Complexity: Towards the Unified Science of Intelligent Communication Networks and SoftwareOperation of autonomic communication networks with complicated user-oriented functions should be described as unreduced many-body interaction process. The latter gives rise to complex-dynamic behaviour including fractally structured hierarchy of chaotically changing realisations. We recall the main results of the universal science of complexity (http://cogprints.org/4471/) based on the unreduced interaction problem solution and its application to various real systems, from nanobiosystems (http://cogprints.org/4527/) and quantum devices to intelligent networks (http://cogprints.org/4114/) and emerging consciousness (http://cogprints.org/3857/). We concentrate then on applications to autonomic communication leading to fundamentally substantiated, exact science of intelligent communication and software. It aims at unification of the whole diversity of complex information system behaviour, similar to the conventional, "Newtonian" science order for sequential, regular models of system dynamics. Basic principles and first applications of the unified science of complex-dynamic communication networks and software are outlined to demonstrate its advantages and emerging practical perspectives.Andrei Kirilyuk2006-07-23Z2011-03-11T08:56:31Zhttp://cogprints.org/id/eprint/4991This item is in the repository with the URL: http://cogprints.org/id/eprint/49912006-07-23ZWhat is Animacy in Dynamical Movement?Hiroyuki KuwamuraTomoyuki YamamotoTakashi Hashimoto2004-10-06Z2011-03-11T08:55:42Zhttp://cogprints.org/id/eprint/3857This item is in the repository with the URL: http://cogprints.org/id/eprint/38572004-10-06ZEmerging Consciousness as a Result of Complex-Dynamical Interaction ProcessA quite general interaction process within a multi-component system is analysed by the extended effective potential method, liberated from usual limitations of perturbation theory or integrable model. The obtained causally complete solution of the many-body problem reveals the phenomenon of dynamic multivaluedness, or redundance, of emerging, incompatible system realisations and dynamic entanglement of system components within each realisation. The ensuing concept of dynamic complexity (and related intrinsic chaoticity) is absolutely universal and can be applied to the problem of consciousness that emerges now as a high enough, properly specified level of unreduced complexity of a suitable interaction process. This complexity level can be identified with the appearance of bound, permanently localised states in the multivalued brain dynamics from strongly chaotic states of unconscious intelligence, by analogy with classical behaviour emergence from quantum states at much lower levels of world dynamics. We show that the main properties of this dynamically emerging consciousness (and intelligence, at the preceding complexity level) correspond to empirically derived properties of natural versions and obtain causally substantiated conclusions about their artificial realisation, including the fundamentally justified paradigm of genuine machine consciousness. This rigorously defined machine consciousness is different from both natural consciousness and any mechanistic, dynamically single-valued imitation of the latter. We use then the same, truly universal concept of complexity to derive equally rigorous conclusions about mental and social implications of the machine consciousness paradigm, demonstrating its indispensable role in the next stage of civilisation development.Andrei Kirilyuk2005-09-18Z2011-03-11T08:56:11Zhttp://cogprints.org/id/eprint/4537This item is in the repository with the URL: http://cogprints.org/id/eprint/45372005-09-18ZA Type of Delay Feedback Control of Chaotic Dynamics in a Chaotic Neural NetworkA chaotic neural network consisting of chaotic neurons exhibits such rich dynamical behaviors
as nonperiodic associative memory. But it is difficult to distinguish the stored
patterns from others, since the chaotic neural network shows chaotic wandering around the stored
patterns. In order to apply the nonperiodic associative memory to information search or pattern
identification, it is necessary to control chaotic dynamics. In this paper, we propose a delay
feedback control method for the chaotic neural network. Computer simulation shows that, by means
of the control method, the chaotic dynamics in the chaotic neural network are changed. The
output sequence of the controlled network wanders around one stored pattern and its reverse pattern.Dr Guoguang HeDr Jouseke KuroiwaProf. Hisakazu OguraDr. Ping ZhuProf. Zhitong CaoDr. Hongping Chen2004-07-06Z2011-03-11T08:55:38Zhttp://cogprints.org/id/eprint/3703This item is in the repository with the URL: http://cogprints.org/id/eprint/37032004-07-06ZComplex Systems Analysis of Arrested Neural Cell Differentiation during Development and Analogous Cell Cycling Models in Carcinogenesis
A new approach to the modular, complex systems analysis of nonlinear dynamics of arrested neural cell Differentiation--induced cell proliferation during organismic development and the analogous cell cycling network transformations involved in carcinogenesis is proposed. Neural tissue arrested differentiation that induces cell proliferation during perturbed development and Carcinogenesis are complex processes that involve dynamically inter-connected biomolecules in the intercellular, membrane, cytosolic, nuclear and nucleolar compartments. Such 'dynamically inter-connected' biomolecules form numerous inter-related pathways referred to as 'molecular networks'. One such family of signaling pathways contains the cell cyclins. Cyclins are proteins that link several critical pro-apoptotic and other cell cycling/division components, including the tumor suppressor gene TP53 and its product, the Thomsen-Friedenreich antigen (T antigen), Rb, mdm2, c-Myc, p21, p27, Bax, Bad and Bcl-2, which play major roles in various neoplastic transformations of many tissues. The novel theoretical analysis presented here is based on recently published studies of arrested cell differentiation that normally leads to neural system formation during early developmental stages; the perturbed development may involve cyclin signaling and cell cycling responsible for rapidly induced cell proliferation without differentiation into neural cells in such experimental studies; special emphasis in this modular model is placed upon the roles of cyclins D1 and E, and does suggest novel clinical trials as well as rational therapies of cancer through re-establishment of cell cycling inhibition in metastatic cancer cells. Cyclins are proteins that are often over-expressed in cancerous cells (Dobashi et al., 2004). They may also be over-expressed in cells whose differentiation is arrested during the early stages of organismic development, leading to increased cell proliferation instead of differentiation into specialized tissues such as those forming the neural system. Cyclin-dependent kinases (CDK), their respective cyclins, and inhibitors of CDKs (CKIs) were identified as instrumental components of the cell cycle-regulating machinery. In mammalian cells the complexes of cyclins D1, D2, D3, A and E with CDKs are considered motors that drive cells to enter and pass through the “S” phase. Cell cycle regulation is a critical mechanism governing cell division and proliferation, and it is finely regulated by the interaction of cyclins with CDKs and CKIs, among other molecules (Morgan et al., 1995). A categorical and Topos framework for Łukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional genomes and cell interactomes is also proposed. Łukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of varying 'next-state' functions is extended in a Łukasiewicz-Topos with an n-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.
Important aspects of Cell Cycling, the Control of Cell Division,and the Neoplastic Transformation in Carcinogenesis are being considered and subjected to algebraic-logico- relational, and computer-aided investigations. The essential roles of various levels of
c-Myc, p27 quasi-complete inhibition/blocking, TP53 and/or p53 inactivation, as well as the perpetual hTERT activation of Telomerase biosynthesis are pointed out as key conditions for Malignant Cell transformations and partial re-differentiation leading to various types of cancer such as lung, breast,skin, prostate and colon. Rational Clinical trials, Individualized Medicine and the potential for optimized Radio-, Chemo-, Gene-, and Immuno- therapies of Cancers are suggested on the basis of integrated complex systems biology modeling of oncogenesis, coupled with extensive genomic/proteomic and interactomic High-throughput/high-sensitivity measurements of identified, sorted cell lines that are being isolated from malignant tumors of patients undergoing clinical trials with adjuvant signaling drug therapies. The implications of the cyclin model for abnormal neural development during early development are being considered in this model that may lead to explanations of subsequent cognitive changes associated with abnormal neural cell differentiation in environmentally-affected embryos. This new model may also be relevant to detecting the onset of senescing neuron transformations in Alzheimer's and related diseases of the human brain in ageing populations at risk.
Professor I.C. Baianuicb1M.S. V Prisecaru2004-07-06Z2011-03-11T08:55:37Zhttp://cogprints.org/id/eprint/3697This item is in the repository with the URL: http://cogprints.org/id/eprint/36972004-07-06ZQuantum Genetics in terms of Quantum Reversible Automata and
Quantum Computation of Genetic Codes and Reverse Transcription
The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In previous publications (Baianu,1971a, b) the formal concept of quantum automaton and quantum computation, respectively, were introduced and their possible implications for genetic processes and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b; 1977; 1987; 2004; Baianu et al, 2004). The notions of topological semigroup, quantum automaton, or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Łukasiewicz Logic Algebras that showed significant dissimilarities (Baianu, 1977; 2004a; Baianu et al, 2004b) from Boolean models of human neural networks (McCullough and Pitts, 1943). Molecular models in terms of categories, functors and natural transformations were then formulated for uni-molecular chemical transformations, multi-molecular chemical and biochemical transformations (Baianu, 1983, 1987, 2004a). Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, oncogenesis and medicine were extensively reviewed and several important conclusions were reached regarding both the potential and limitations of the computation-assisted modeling of biological systems, and especially complex organisms such as Homo sapiens sapiens (Baianu,1987). Novel approaches to solving the realization problems of Relational Biology models in Complex System Biology are introduced in terms of natural transformations between functors of such molecular categories. Several applications of such natural transformations of functors were then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Topoi of Łukasiewicz Logic Algebras and Intuitionistic Logic (Heyting) Algebras are being considered for modeling nonlinear dynamics and cognitive processes in complex neural networks that are present in the human brain, as well as stochastic modeling of genetic networks in Łukasiewicz Logic Algebras.
Professor I.C. Baianuicb12004-09-09Z2011-03-11T08:55:41Zhttp://cogprints.org/id/eprint/3815This item is in the repository with the URL: http://cogprints.org/id/eprint/38152004-09-09ZSystems with inheritance: dynamics of distributions with conservation of support, natural selection and finite-dimensional asymptoticsIf we find a representation of an infinite-dimensional dynamical system as a nonlinear kinetic system with {\it conservation of supports} of distributions, then (after some additional technical steps) we can state that the asymptotics is finite-dimensional. This conservation of support has a {\it quasi-biological interpretation, inheritance} (if a gene was not presented initially in a isolated population without mutations, then it cannot appear at later time). These quasi-biological models can describe various physical, chemical, and, of course, biological
systems. The finite-dimensional asymptotic demonstrates effects of {\it ``natural" selection}. The estimations of asymptotic dimension are presented. The support of an individual limit distribution is almost always small. But the union of such supports can be the whole space even for one solution. Possible are such situations: a solution is a finite set of narrow peaks getting in time more and more narrow, moving slower and slower. It is possible that these peaks do not tend to fixed positions, rather they continue moving, and the path covered tends to infinity at $t \rightarrow \infty$. The {\it drift equations} for peaks motion are obtained. Various types of stability are studied.
In example, models of cell division self-synchronization are
studied. The appropriate construction of notion of typicalness in infinite-dimensional spaces is discussed, and the ``completely thin" sets are introduced.
A.N. Gorban2004-07-06Z2011-03-11T08:55:37Zhttp://cogprints.org/id/eprint/3701This item is in the repository with the URL: http://cogprints.org/id/eprint/37012004-07-06ZŁukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models
A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Łukasiewicz Topos with an n-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.
Professor I.C. Baianuicb12004-07-06Z2011-03-11T08:55:37Zhttp://cogprints.org/id/eprint/3676This item is in the repository with the URL: http://cogprints.org/id/eprint/36762004-07-06ZQuantum Genetics, Quantum Automata and Quantum Computation
The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In a previous publication (Baianu,1971a) the formal concept of quantum automaton was introduced and its possible implications for genetic and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b). The notions of topological semigroup, quantum automaton,or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Lukasiewicz Logic Algebras that showed significant dissimilarities (Baianu, 1977) from Bolean models of human neural networks (McCullough and Pitts,1945). Molecular models in terms of categories, functors and natural transformations were then formulated for uni-molecular chemical transformations, multi-molecular chemical and biochemical transformations (Baianu, 1983,2004a). Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, oncogenesis and medicine were extensively reviewed and several important conclusions were reached regarding both the potential and limitations of the computation-assisted modeling of biological systems, and especially complex organisms such as Homo sapiens sapiens(Baianu,1987). Novel approaches to solving the realization problems of Relational Biology models in Complex System Biology are introduced in terms of natural transformations between functors of such molecular categories. Several applications of such natural transformations of functors were then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Other possible realizations in Molecular Biology and Relational Biology of Organisms are here suggested in terms of quantum automata models of Quantum Genetics and Interactomics. Future developments of this novel approach are likely to also include: Fuzzy Relations in Biology and Epigenomics, Relational Biology modeling of Complex Immunological and Hormonal regulatory systems, n-categories and Topoi of Lukasiewicz Logic Algebras and Intuitionistic Logic (Heyting) Algebras for modeling nonlinear dynamics and cognitive processes in complex neural networks that are present in the human brain, as well as stochastic modeling of genetic networks in Lukasiewicz Logic Algebras.
Professor I. C. Baianuicb2004-05-24Z2011-03-11T08:55:36Zhttp://cogprints.org/id/eprint/3641This item is in the repository with the URL: http://cogprints.org/id/eprint/36412004-05-24ZSocial Balance Theory: Revisiting Heider’s Balance Theory for many agents
We construct a model based on Heider’s social balance theory to analyze the interpersonal network among social agents. The model of social balance theory provides us an interesting tool to see how a social group evolves to the possible balance state. We introduce the balance index that can be used to measure social balance in macro structure level (global balance index) or in micro structure (local balance index) to see how the local balance index influences the global balance structure. Several experiments are done and we discover how the social group can form separation of subgroups in a group or strengthening a social group while emphasizing the structure theorem and social mitosis previously introduced. Mr Hokky SitungkirMr Deni Khanafiah2004-12-15Z2011-03-11T08:55:47Zhttp://cogprints.org/id/eprint/3988This item is in the repository with the URL: http://cogprints.org/id/eprint/39882004-12-15ZComplex Dynamics of Autonomous Communication Networks and the Intelligent Communication ParadigmDynamics of arbitrary communication system is analysed as unreduced interaction process. The applied generalised, universally nonperturbative method of effective potential reveals the phenomenon of dynamic multivaluedness of competing system configurations forced to permanently replace each other in a causally random order, which leads to universally defined dynamical chaos, complexity, fractality, self-organisation, and adaptability. We demonstrate the origin of huge, exponentially high efficiency of the unreduced, complex network dynamics and specify the universal symmetry of complexity as the fundamental guiding principle for creation and control of such qualitatively new kind of networks and devices. The emerging intelligent communication paradigm and its practical realisation in the form of knowledge-based networks involve the properties of true, unreduced intelligence and consciousness (http://cogprints.ecs.soton.ac.uk/archive/00003857/) appearing in the complex (multivalued) network dynamics and results.
Dr. Andrei Kirilyuk2005-03-06Z2011-03-11T08:55:51Zhttp://cogprints.org/id/eprint/4114This item is in the repository with the URL: http://cogprints.org/id/eprint/41142005-03-06ZComplex Dynamics of Autonomous Communication Networks and the Intelligent Communication ParadigmDynamics of arbitrary communication system is analysed as unreduced interaction process. The applied generalised, universally nonperturbative method of effective potential reveals the phenomenon of dynamic multivaluedness of competing system configurations forced to permanently replace each other in a causally random order, which leads to universally defined dynamical chaos, complexity, fractality, self-organisation, and adaptability. We demonstrate the origin of huge, exponentially high efficiency of the unreduced, complex network dynamics and specify the universal symmetry of complexity as the fundamental guiding principle for creation and control of such qualitatively new kind of networks and devices. The emerging intelligent communication paradigm and its practical realisation in the form of knowledge-based networks involve the properties of true, unreduced intelligence and consciousness (http://cogprints.ecs.soton.ac.uk/archive/00003857/) appearing in the complex (multivalued) network dynamics and results.
Andrei Kirilyuk2004-03-17Z2011-03-11T08:55:30Zhttp://cogprints.org/id/eprint/3500This item is in the repository with the URL: http://cogprints.org/id/eprint/35002004-03-17ZEPIDEMIOLOGY THROUGH CELLULAR AUTOMATAThis paper performs the utilization of cellular automata computational analysis as the dynamic model of spatial epidemiology. Here, explored elementary aspects of cellular automata and its application in analyzing contagious disease, in this case avian influenza disease in Indonesia. Computational model is built and map-based simulation is performed using several simplified data of such transportation through sea in Indonesia, and its accordance with poultries in Indonesia, with initial condition of notified avian influenza infected area in Indonesia. The initial places are Pekalongan, West Java, East Java, and several regions in Sumatera. The result of simulation is showing the spreading-rate of influenza and in simple way and describing possible preventive action through isolation of infected areas as a major step of preventing pandemic.
Hokky Situngkir2006-07-01Z2011-03-11T08:56:28Zhttp://cogprints.org/id/eprint/4950This item is in the repository with the URL: http://cogprints.org/id/eprint/49502006-07-01ZMay We Have Your Attention: Analysis of a Selective Attention TaskIn this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evolve behavior that generalizes well across subproblems, and demonstrate a shaping protocol that improves generalization.Eldan GoldenbergJacob R GarcowskiRandall D Beer2003-12-18Z2011-03-11T08:55:24Zhttp://cogprints.org/id/eprint/3319This item is in the repository with the URL: http://cogprints.org/id/eprint/33192003-12-18ZBrain-inspired conscious computing architectureWhat type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human.
Prof Wlodzislaw Duch2005-08-20Z2011-03-11T08:56:09Zhttp://cogprints.org/id/eprint/4506This item is in the repository with the URL: http://cogprints.org/id/eprint/45062005-08-20ZAs contribuições da ciência cognitiva à composição musicalThis dissertation’s goal is to construct a detailed map of the principal branches of
cognitive science and their methodological and epistemological contributions to the study
music composition. We are concerned, firstly, with the contributions to the compositional
techniques, and secondly, with their perception. The first chapter deals with the cognitivist
paradigm by means of artificial intelligence. In the second chapter we relate the artificial
intelligence with the music composition, investigating the cognitvist models of composition
by the analysis of automatic compositional systems. The third chapter brings the artificial
neural networks to the scene, within the so-called connectionist paradigm. In our fourth
chapter we established the relation between the connectionism and music composition. In this
sense, we describe implementations that model and/or simulate aspects of perception and
composition. The fifth chapter leaves the computational perspective in the study of cognition
and present alternative proposals in this sense, related to the music composition and
musicology, as the ecological approach to auditory perception and the theories of
emergentism applied to music.Luis F Oliveira2004-01-27Z2011-03-11T08:55:27Zhttp://cogprints.org/id/eprint/3406This item is in the repository with the URL: http://cogprints.org/id/eprint/34062004-01-27ZAs contribuições da ciência cognitiva à composição musicalThis dissertation’s goal is to construct a detailed map of the principal branches of
cognitive science and their methodological and epistemological contributions to the study
music composition. We are concerned, firstly, with the contributions to the compositional
techniques, and secondly, with their perception. The first chapter deals with the cognitivist
paradigm by means of artificial intelligence. In the second chapter we relate the artificial
intelligence with the music composition, investigating the cognitvist models of composition
by the analysis of automatic compositional systems. The third chapter brings the artificial
neural networks to the scene, within the so-called connectionist paradigm. In our fourth
chapter we established the relation between the connectionism and music composition. In this
sense, we describe implementations that model and/or simulate aspects of perception and
composition. The fifth chapter leaves the computational perspective in the study of cognition
and present alternative proposals in this sense, related to the music composition and
musicology, as the ecological approach to auditory perception and the theories of
emergentism applied to music.Luis F Oliveira2005-09-18Z2011-03-11T08:56:11Zhttp://cogprints.org/id/eprint/4538This item is in the repository with the URL: http://cogprints.org/id/eprint/45382005-09-18ZControlling Chaos in a Neural Network Based on the Phase Space ConstraintThe chaotic neural network constructed with chaotic neurons exhibits very rich dynamic
behaviors and has a nonperiodic associative memory. In the chaotic neural network,
however, it is dicult to distinguish the stored patters from others, because the states of
output of the network are in chaos. In order to apply the nonperiodic associative memory
into information search and pattern identication, etc, it is necessary to control chaos in
this chaotic neural network. In this paper, the phase space constraint method focused on
the chaotic neural network is proposed. By analyzing the orbital of the network in phase
space, we chose a part of states to be disturbed. In this way, the evolutional spaces of
the strange attractors are constrained. The computer simulation proves that the chaos
in the chaotic neural network can be controlled with above method and the network can
converge in one of its stored patterns or their reverses which has the smallest Hamming
distance with the initial state of the network. The work claries the application prospect
of the associative dynamics of the chaotic neural network.Dr Guo-guang HEProf. Zhi-tong CAODr. Hong-ping CHENDr Ping ZHU2004-03-18Z2011-03-11T08:55:30Zhttp://cogprints.org/id/eprint/3507This item is in the repository with the URL: http://cogprints.org/id/eprint/35072004-03-18ZOn Massive Conflict: Macro-Micro LinkMicro and macro properties of social system should be taken as relative poles of a two dimensional continuum since every debate on social system will however shift to the discussion on the two levels of description. This is consistently used as perspective to see massive social conflict. We propose analysis of the emerging conflict on its micro-causations by using computer simulations. We construct a dynamical model based on some propositions on massive conflict based upon the individual’s degree of membership to collective identity she has whether to mobilize or not. The simulations result the possibilities to see the linkage of the macro-micro properties in the case of massive conflict and suggestions on how to cope with massive conflict or even to resolve it. The paper is an endeavor to a more comprehensive methodology on how to cope with conflict on research and theory development.Hokky Situngkir2004-02-12Z2011-03-11T08:55:25Zhttp://cogprints.org/id/eprint/3336This item is in the repository with the URL: http://cogprints.org/id/eprint/33362004-02-12ZCollaboration Development through Interactive Learning between Human and RobotIn this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions.Tetsuya OgataNoritaka MasagoShigeki SuganoJun Tani2003-11-03Z2011-03-11T08:55:23Zhttp://cogprints.org/id/eprint/3261This item is in the repository with the URL: http://cogprints.org/id/eprint/32612003-11-03ZCompact Integrated Transconductance Amplifier Circuit for Temporal DifferentiationA compact integrated CMOS circuit for temporal differentiation is presented. It consists of a high-gain inverting amplifier, an active non-linear transconductance and a capacitor and requires only 4 transistors in its minimal configuration.The circuit provides two rectified current outputs that are proportional to the temporal derivative of the input voltage signal. Besides the compactness of its design, the presented circuit is not dependent on the DC-value of the input signal, as compared with known integrated differentiator circuits. Measured chip results show that the circuit operates on a large input frequency range for which it provides nearideal temporal differentiation. The circuit is particularly suited for focal-plane implementations of gradient-based visual motion systems.Dr. Alan Stocker2003-08-08Z2011-03-11T08:55:19Zhttp://cogprints.org/id/eprint/3087This item is in the repository with the URL: http://cogprints.org/id/eprint/30872003-08-08ZConstructive Methods of Invariant Manifolds for Kinetic ProblemsWe present the Constructive Methods of Invariant Manifolds for model reduction in physical and chemical kinetics, developed during last two decades. The physical problem of reduced description is studied in a most general form as a problem of constructing the slow invariant manifold. The invariance conditions are formulated as the differential equation for a manifold immersed in the phase space (the invariance equation). The equation of motion for immersed manifolds is obtained (the film extension of the dynamics). Invariant manifolds are fixed points for this equation, and slow invariant manifolds are Lyapunov stable fixed points, thus slowness is presented as stability. A collection of methods for construction of slow invariant manifolds is presented, in particular, the Newton method subject to incomplete linearization is the analogue of KAM methods for dissipative systems.
The systematic use of thermodynamics structures and of the quasi--chemical representation allow to construct approximations which are in concordance with physical restrictions.
We systematically consider a discrete analogue of the slow (stable) positively invariant manifolds for dissipative systems, invariant grids. Dynamic and static postprocessing procedures give us the opportunity to estimate the accuracy of obtained approximations, and to improve this accuracy significantly.
The following examples of applications are presented: Nonperturbative deviation of physically consistent hydrodynamics from the Boltzmann equation and from the reversible dynamics, for Knudsen numbers Kn~1; construction of the moment equations for nonequilibrium media and their dynamical correction (instead of extension of list of variables) to gain more accuracy in description of highly nonequilibrium flows; determination of molecules dimension (as diameters of equivalent hard spheres) from experimental viscosity data; invariant grids for a two-dimensional catalytic reaction and a four-dimensional oxidation reaction (six species, two balances); universal continuous media description of dilute polymeric solution; the limits of macroscopic description for polymer molecules, etc.
Prof Alexander N. GorbanDr. Iliya V. KarlinDr. Andrei Yu. Zinovyev2004-04-06Z2011-03-11T08:55:17Zhttp://cogprints.org/id/eprint/3001This item is in the repository with the URL: http://cogprints.org/id/eprint/30012004-04-06ZControlling chaos in a chaotic neural networkThe chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.Dr G. HeggheProf. Z. CaoProf. P. ZhuProf. H. Ogura2004-02-12Z2011-03-11T08:55:25Zhttp://cogprints.org/id/eprint/3330This item is in the repository with the URL: http://cogprints.org/id/eprint/33302004-02-12ZA Dynamical Analysis of Kneading Using a Motion Capture DevicePhysical skills such as playing the musical instrument are hard to learn and take long time
to master. To investigate what makes physical
skills so dificult to learn and how we can evaluate the level of skills, we examined the kneading
in ceramic art, an action to prepare the clay for
shaping and studied the physical movements of
both the learners and experts.
Kneading is an appropriate sample of physical
skill for studying the body movement because all
the parts of body need to be coordinated to accomplish the task. The task is not hopelessly difficult for the complete novices to follow the instruction although the end result is not satisfactory.
It normally takes about three years to master the
kneading skill. It is also relatively easy to judge
how well the subjects accomplished the task by
observing the shape of the clay.
After careful examination of the movement using video tapes, we employed a motion capture
device to collect the data of movement from an
expert, an experienced person, and three novices.
We discovered that the expert elegantly splits his
body into two parts, torso and arms, and effectively coordinates these two parts while kneading
the clay.Mamiko AbeTomoyuki YamamotoTsutomu Fujinami2004-02-13Z2011-03-11T08:55:28Zhttp://cogprints.org/id/eprint/3435This item is in the repository with the URL: http://cogprints.org/id/eprint/34352004-02-13ZFrom Analogue to Digital VocalizationsSound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents.Dr. Pierre-Yves Oudeyer2005-02-22Z2011-03-11T08:55:51Zhttp://cogprints.org/id/eprint/4108This item is in the repository with the URL: http://cogprints.org/id/eprint/41082005-02-22ZFrom Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker HypothesisSound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents.Pierre-Yves Oudeyer2003-04-16Z2011-03-11T08:55:15Zhttp://cogprints.org/id/eprint/2888This item is in the repository with the URL: http://cogprints.org/id/eprint/28882003-04-16ZSelf-Replicating Machines in Continuous Space with Virtual PhysicsJohnnyVon is an implementation of self-replicating machines in
continuous two-dimensional space. Two types of particles drift
about in a virtual liquid. The particles are automata with
discrete internal states but continuous external relationships.
Their internal states are governed by finite state machines but
their external relationships are governed by a simulated physics
that includes Brownian motion, viscosity, and spring-like attractive
and repulsive forces. The particles can be assembled into patterns
that can encode arbitrary strings of bits. We demonstrate that, if
an arbitrary "seed" pattern is put in a "soup" of separate individual
particles, the pattern will replicate by assembling the individual
particles into copies of itself. We also show that, given sufficient
time, a soup of separate individual particles will eventually
spontaneously form self-replicating patterns. We discuss the implications
of JohnnyVon for research in nanotechnology, theoretical biology, and
artificial life.Arnold SmithPeter Turney2175Robert Ewaschuk2002-08-31Z2011-03-11T08:54:59Zhttp://cogprints.org/id/eprint/2440This item is in the repository with the URL: http://cogprints.org/id/eprint/24402002-08-31ZJohnnyVon: Self-Replicating Automata in Continuous Two-Dimensional SpaceJohnnyVon is an implementation of self-replicating automata in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary “seed” pattern is put in a “soup” of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life.Arnold SmithPeter Turneypeter.turneyRobert Ewaschuk2002-09-17Z2011-03-11T08:55:00Zhttp://cogprints.org/id/eprint/2465This item is in the repository with the URL: http://cogprints.org/id/eprint/24652002-09-17ZSorting Methods in Self-Organization of Models and Clusterizations (Review of New Basic Ideas) - Iterative (Multirow) Polynomial GMDH AlgorithmsReview of the Group Method of Data Handling approachA.G. Ivakhnenko2006-05-25Z2011-03-11T08:56:25Zhttp://cogprints.org/id/eprint/4882This item is in the repository with the URL: http://cogprints.org/id/eprint/48822006-05-25ZModelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational IntelligenceThe main objective of the present work is to develop a framework for modelling and controlling of a real world multi-input and multi-output (MIMO) continuously drifting metallurgical process, which is shown to be a complex system. A small change in the properties of the charge composition may lead to entirely different outcome of the process. The newly emerging paradigm of soft-computing or Hybrid Computational Intelligence Systems approach which is based on neural networks, fuzzy sets, genetic algorithms and chaos theory has been applied to tackle this problem In this framework first a feed-forward neuro-model has been developed based on the data collected from a working Submerged Arc Furnace (SAF). Then the process is analysed for the existence of the chaos with the chaos theory (calculating indices like embedding dimension, Lyapunov exponent etc). After that an effort is made to evolve a fuzzy logic controller for the dynamical process using combination of genetic algorithms and the neural networks based forward model to predict the system’s behaviour or conditions in advance and to further suggest modifications to be made to achieve the desired results.Krishnaiah Jalluj.krishnaiah @ gmail.comS Kumar CA K RoyM Aslam Faruqi2002-12-17Z2011-03-11T08:55:07Zhttp://cogprints.org/id/eprint/2663This item is in the repository with the URL: http://cogprints.org/id/eprint/26632002-12-17ZAdaptivity through alternate freeing and freezing of degrees of freedomStarting with fewer degrees of freedom has been shown to enable a more efficient exploration of the sensorimotor space. While not necessarily leading to optimal task performance, it results in a smaller number of directions of stability, which guide the coordination of additional degrees of freedom. The developmental release of additional degrees of freedom is then expected to allow for optimal task performance and more tolerance and adaptation to environmental interaction. In this paper, we test this assumption with a small-sized humanoid robot that learns to swing under environmental perturbations. Our experiments show that a progressive release of degrees of freedom alone is not sufficient to cope with environmental perturbations. Instead, alternate freezing and freeing of the degrees of freedom is required. Such finding is consistent with observations made during transitional periods in acquisition of skills in infants.
Max LungarellaDr Luc Berthouze14632002-07-15Z2011-03-11T08:54:57Zhttp://cogprints.org/id/eprint/2319This item is in the repository with the URL: http://cogprints.org/id/eprint/23192002-07-15ZClassification of Random Boolean NetworksWe provide the first classification of different types of
Random Boolean Networks (RBNs). We study the differences
of RBNs depending on the degree of synchronicity and
determinism of their updating scheme. For doing so, we first
define three new types of RBNs. We note some similarities
and differences between different types of RBNs with the aid
of a public software laboratory we developed. Particularly, we
find that the point attractors are independent of the updating
scheme, and that RBNs are more different depending on their
determinism or non-determinism rather than depending on
their synchronicity or asynchronicity. We also show a way of
mapping non-synchronous deterministic RBNs into
synchronous RBNs. Our results are important for justifying
the use of specific types of RBNs for modelling natural
phenomena.
Carlos Gershenson2002-01-11Z2011-03-11T08:54:52Zhttp://cogprints.org/id/eprint/2014This item is in the repository with the URL: http://cogprints.org/id/eprint/20142002-01-11ZComputer simulation: A new scientific approach to the study of language evolution(summary of the whole book)
This volume provides a comprehensive survey of computational models and methodologies used for studying the origin and evolution of language and communication. With contributions from the most influential figures in the field, Simulating the Evolution of Language presents and summarises current computational approaches to language evolution and highlights new lines of development. Among the main discussion points are:
· Analysis of emerging linguistic behaviours and structures
· Demonstration of the strict interaction and interdependence between language and other non-linguistic abilities
· Direct comparisons between simulation studies and empirical research
Essential reading for researchers and students in the areas of evolutionary and adaptive systems, language evolution, modelling and linguistics, it will also be of particular interest to computer scientists working on multi-agent systems, robotics and internet agents.
Angelo CangelosiDomenico Parisi2003-10-04Z2011-03-11T08:55:21Zhttp://cogprints.org/id/eprint/3167This item is in the repository with the URL: http://cogprints.org/id/eprint/31672003-10-04ZGeometry of irreversibility: The film of nonequilibrium statesA general geometrical framework of nonequilibrium thermodynamics is developed. The notion of macroscopically definable ensembles is developed. The thesis about macroscopically definable ensembles is suggested. This thesis should play the same role in the nonequilibrium thermodynamics, as the Church-Turing thesis in the theory of computability. The primitive macroscopically definable ensembles are described. These are ensembles with macroscopically prepared initial states.
The method for computing trajectories of primitive macroscopically definable nonequilibrium ensembles is elaborated. These trajectories are represented as sequences of deformed equilibrium ensembles and simple quadratic models between them. The primitive macroscopically definable ensembles form the manifold in the space of ensembles. We call this manifold the film of nonequilibrium states.
The equation for the film and the equation for the ensemble motion on the film are written down. The notion of the invariant film of non-equilibrium states, and the method of its approximate construction transform the problem of nonequilibrium kinetics into a series of problems of equilibrium statistical physics. The developed methods allow us to solve the problem of macro-kinetics even when there are no autonomous equations of macro-kinetics.
Alexander N. GorbanIliya V. Karlin2003-10-04Z2011-03-11T08:55:04Zhttp://cogprints.org/id/eprint/2534This item is in the repository with the URL: http://cogprints.org/id/eprint/25342003-10-04ZGlobal Dynamics: a new concept for design of dynamical behaviorThe global dynamics, a novel concept for design of human/humanoid behavior is proposed. The principle of this concept is to exploit the body dynamics and apply control input only where it is necessary.
Within the phase space of the body dynamics, there are many stable and unstable mani-folds coexist. Then if we analysed its structure and obtained a map in sufficient resolution, it may be possible to realise a motion by exploiting stable regions for reducing control input and unstable regions for switching between stable regions.
Also, we expect an emergence of symbols within the dynamics, as the series of points where control input should be adopted. This feature realises higher level description and makes adaptation behavior easier. We are studying from two aspects, the motion capture experiment and dynamical simulation of simple elastic robot. The former supports that above assumption and the latter supports the exploiting the dynamical stability is useful.Tomoyuki YamamotoYasuo Kuniyoshi2003-07-25Z2011-03-11T08:55:19Zhttp://cogprints.org/id/eprint/3085This item is in the repository with the URL: http://cogprints.org/id/eprint/30852003-07-25ZInformation processing and dynamical systems approaches are complementaryShanker and King trumpet the adoption of a ‘new paradigm’ in communication studies, exemplified by Ape Language Research. While cautiously sympathetic, I maintain that their argument relies on a false dichotomy between ‘information’ and ‘dynamical systems’ theory, and that the resulting confusion prevents them from seeing and taking the main chance their line of thinking suggests.Dr David Spurrett4662003-03-12Z2011-03-11T08:55:07Zhttp://cogprints.org/id/eprint/2658This item is in the repository with the URL: http://cogprints.org/id/eprint/26582003-03-12ZPhonemic Coding Might Result From
Sensory-Motor Coupling DynamicsHuman sound systems are invariably phonemically coded. Furthermore,
phoneme inventories follow very particular tendancies. To explain
these phenomena, there existed so far three kinds of approaches :
``Chomskyan''/cognitive innatism, morpho-perceptual innatism
and the more recent approach of ``language as a complex cultural system
which adapts under the pressure of efficient communication''.
The two first approaches are clearly not satisfying, while
the third, even if much more convincing,
makes a lot of speculative assumptions and did not
really bring answers to the question of phonemic coding. We propose
here a new hypothesis based on a low-level model of
sensory-motor interactions. We show that certain very
simple and non language-specific neural devices
allow a population of agents to build signalling systems
without any functional pressure. Moreover, these systems
are phonemically coded. Using a realistic vowel articulatory
synthesizer, we show that the inventories of vowels
have striking similarities with human vowel systems.Pierre-Yves Oudeyer2003-09-19Z2011-03-11T08:55:20Zhttp://cogprints.org/id/eprint/3151This item is in the repository with the URL: http://cogprints.org/id/eprint/31512003-09-19ZIntelligent systems in the context of surrounding environmentWe investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons.
In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment).
Source code for the programs used can be found at http://neuro.webdrake.net/.Joseph WakelingJWakelingPer Bak2005-02-01Z2011-03-11T08:55:49Zhttp://cogprints.org/id/eprint/4043This item is in the repository with the URL: http://cogprints.org/id/eprint/40432005-02-01ZIntelligent systems in the context of surrounding environmentWe investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons.
In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment).
Source code for the programs used can be found at http://neuro.webdrake.net/.Joseph WakelingJWakelingPer Bak2002-06-10Z2011-03-11T08:54:56Zhttp://cogprints.org/id/eprint/2249This item is in the repository with the URL: http://cogprints.org/id/eprint/22492002-06-10ZAttentional and Semantic AnticipationsWhy are attentional processes important in the driving of anticipations? Anticipatory processes are fundamental cognitive abilities of living systems, in order to rapidly and accurately perceive new events in the environment, and to trigger adapted behaviors to the newly perceived events. To process anticipations adapted to sequences of various events in complex environments, the cognitive system must be able to run specific anticipations on the basis of selected relevant events. Then more attention must be given to events potentially relevant for the living system, compared to less important events.
What are useful attentional factors in anticipatory processes? The relevance of events in the environment depend on the effects they can have on the survival of the living system. The cognitive system must then be able to detect relevant events to drive anticipations and to trigger adapted behaviors. The attention given to an event depends on i) its external physical relevance in the environment, such as time duration and visual quality, and ii) on its internal semantic relevance in memory, such as knowledge about the event (semantic field in memory) and anticipatory power (associative strength to anticipated associates).
How can we model interactions between attentional and semantic anticipations? Specific types of distributed recurrent neural networks are able to code temporal sequences of events as associated attractors in memory. Particular learning protocol and spike rate transmission through synaptic associations allow the model presented to vary attentionally the amount of activation of anticipations (by activation or inhibition processes) as a function of the external and internal relevance of the perceived events. This type of model offers a unique opportunity to account for both anticipations and attention in unified terms of neural dynamics in a recurrent network.
Frédéric LavigneSylvain Denis2002-01-22Z2011-03-11T08:54:52Zhttp://cogprints.org/id/eprint/2043This item is in the repository with the URL: http://cogprints.org/id/eprint/20432002-01-22ZEmbodiment is meaningless without adequate neural dynamicsTraditionally, cognition has been considered as a ``mental processes'' only domain. Recently, however, there is a growing conscensus that cognition should be ``embodied'', i.e. it emerges from physical interaction with the world through a body with given perceptual and motoric abilities. Terms like ``emergence'', ``enaction'', ``grounding'', and ``situatedness'' are often used, but little attention is being paid to actually understanding the neural dynamics correlates of an emergence of cognition. Nor is hardly being investigated how the structure of the body-environment coupling is perceived and manipulated by our brain. It is as if talking about neural dynamics would somehow throw us backwards to the old cognitivist approach. In this paper we present a balanced view, in which we try to keep things in their respective place.Luc BerthouzeAdriaan Tijsseling2001-06-19Z2011-03-11T08:54:42Zhttp://cogprints.org/id/eprint/1623This item is in the repository with the URL: http://cogprints.org/id/eprint/16232001-06-19ZExplaining the Mind: Problems, ProblemsThe mind/body problem is the feeling/function problem: How and why do
feeling systems feel? The problem is not just "hard" but insoluble (unless one
is ready to resort to telekinetic dualism). Fortunately, the "easy" problems of
cognitive science (such as the how and why of categorization and language)
are not insoluble. Five books (by Damasio, Edelman/Tononi, McGinn,
Tomasello and Fodor) are reviewed in this context.Stevan Harnad2002-06-10Z2011-03-11T08:54:56Zhttp://cogprints.org/id/eprint/2248This item is in the repository with the URL: http://cogprints.org/id/eprint/22482002-06-10ZAnticipatory Semantic ProcessesWhy anticipatory processes correspond to cognitive abilities of living systems? To be adapted to an environment, behaviors need at least i) internal representations of events occurring in the external environment; and ii) internal anticipations of possible events to occur in the external environment. Interactions of these two opposite but complementary cognitive properties lead to various patterns of experimental data on semantic processing.
How to investigate dynamic semantic processes? Experimental studies in cognitive psychology offer several interests such as: i) the control of the semantic environment such as words embedded in sentences; ii) the methodological tools allowing the observation of anticipations and adapted oculomotor behavior during reading; and iii) the analyze of different anticipatory processes within the theoretical framework of semantic processing.
What are the different types of semantic anticipations? Experimental data show that semantic anticipatory processes involve i) the coding in memory of sequences of words occurring in textual environments; ii) the anticipation of possible future words from currently perceived words; and iii) the selection of anticipated words as a function of the sequences of perceived words, achieved by anticipatory activations and inhibitory selection processes.
How to modelize anticipatory semantic processes? Localist or distributed neural networks models can account for some types of semantic processes, anticipatory or not. Attractor neural networks coding temporal sequences are presented as good candidate for modeling anticipatory semantic processes, according to specific properties of the human brain such as i) auto-associative memory; ii) learning and memorization of sequences of patterns; and iii) anticipation of memorized patterns from previously perceived patterns.
Frédéric LavignePascal Lavigne2005-04-24Z2011-03-11T08:55:59Zhttp://cogprints.org/id/eprint/4275This item is in the repository with the URL: http://cogprints.org/id/eprint/42752005-04-24ZA QUANTITATIVE MODEL OF THE AMPLIFICATION OF POWER THROUGH ORDER AND THE CONCEPT OF GROUP DEFENSEI propose a simple quantitative model of how the power of a leader over a group is amplified when he or she starts to order the group. This model implies that a small well-informed minority can easily govern a previously ordered majority such as hijacked passengers. The model leads to the concept, “group defense,” which stresses the importance of group members resisting enemy ordering and creating a counter-ordering. Group defense may be helpful in preventing fatal hijackings such as the ones that occurred on September 11 and other massacres on civilians.Dr. Eugen Tarnow2003-12-18Z2011-03-11T08:55:25Zhttp://cogprints.org/id/eprint/3320This item is in the repository with the URL: http://cogprints.org/id/eprint/33202003-12-18ZTherapeutic applications of computer models of brain activity for Alzheimer disease. THERAPEUTIC IMPLICATIONS OF COMPUTER MODELS OF BRAIN ACTIVITY FOR ALZHEIMER DISEASE.Prof Wlodzislaw Duch2002-10-22Z2011-03-11T08:55:05Zhttp://cogprints.org/id/eprint/2547This item is in the repository with the URL: http://cogprints.org/id/eprint/25472002-10-22ZAmplifying Phenomenal Information: Toward a Fundamental Theory of ConsciousnessFundamental approaches bypass the problem of getting consciousness from non-conscious components by positing that consciousness is a universal primitive. For example, the double aspect theory of information holds that information has a phenomenal aspect. How then do you get from phenomenal information to human consciousness? This paper proposes that an entity is conscious to the extent it amplifies information, first by trapping and integrating it through closure, and second by maintaining dynamics at the edge of chaos through simultaneous processes of divergence and convergence. The origin of life through autocatalytic closure, and the origin of an interconnected worldview through conceptual closure, induced phase transitions in the degree to which information, and thus consciousness, is locally amplified. Divergence and convergence of cognitive information may involve phenomena observed in light e.g. focusing, interference, and resonance. By making information flow inward-biased, closure shields us from external consciousness; thus the paucity of consciousness may be an illusion.
Liane Gabora2001-05-09Z2011-03-11T08:54:38Zhttp://cogprints.org/id/eprint/1488This item is in the repository with the URL: http://cogprints.org/id/eprint/14882001-05-09ZThe What and Why of Binding: The Modeler's PerspectiveIn attempts to formulate a computational understanding of brain function,
one of the fundamental concerns is the data structure by which the brain
represents information. For many decades, a conceptual framework has
dominated the thinking of both brain modelers and neurobiologists. That
framework is referred to here as "classical neural networks." It is well
supported by experimental data, although it may be incomplete. A
characterization of this framework will be offered in the next section.
Difficulties in modeling important functional aspects of the brain on the
basis of classical neural networks alone have led to the recognition that
another, general mechanism must be invoked to explain brain function. That
mechanism I call "binding." Binding by neural signal synchrony had been
mentioned several times in the liter ature (Lege´ndy, 1970; Milner, 1974)
before it was fully formulated as a general phenomenon (von der Malsburg,
1981). Although experimental evidence for neural syn chrony was soon found,
the idea was largely ignored for many years. Only recently has it become a
topic of animated discussion. In what follows, I will summarize the nature
and the roots of the idea of binding, especially of temporal binding, and
will discuss some of the objec tions raised against it.
Christoph von der Malsburg1999-06-28Z2011-03-11T08:54:02Zhttp://cogprints.org/id/eprint/545This item is in the repository with the URL: http://cogprints.org/id/eprint/5451999-06-28ZChallenging the Computational Metaphor: Implications for How We ThinkThis paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think.Lynn Andrea Stein1999-04-08Z2011-03-11T08:54:02Zhttp://cogprints.org/id/eprint/534This item is in the repository with the URL: http://cogprints.org/id/eprint/5341999-04-08ZThe Cost of Rational AgencyThe rational agency assumption limits systems to domains of application that have never been observed. Moreover, representing agents as being rational in the sense of maximising utility subject to some well specified constraints renders software systems virtually unscalable. These properties of the rational agency assumption are shown to be unnecessary in representations or analogies of markets. The demonstration starts with an analysis of how the rational agency assumption limits the applicability and scalability of the IBM information filetering economy. An unrestricted specification of the information filtering economy is developed from an analysis of the properties of markets as systems and the implementation of a model based on intelligent agents. This extended information filtering economy modelis used to test the analytical results on the scope for agents to act as intermediaries between human users and information sources.Scott Moss1999-10-06Z2011-03-11T08:54:02Zhttp://cogprints.org/id/eprint/547This item is in the repository with the URL: http://cogprints.org/id/eprint/5471999-10-06ZDynamical recurrent neural networks towards prediction and modeling of dynamical systemsThis paper addresses the use of Dynamical Recurrent Neural Networks (DRNN) for time series prediction and modeling of small dynamical systems. Since the recurrent synapses are represented by Finite Impulse Response (FIR) filters, DRNN are state-based connectionist models in which all hidden units act as state variables of a dynamical system. The model is trained with Temporal Recurrent Backprop (TRBP), an efficient backward recurrent training procedure with minimal computational burden which benefits from the exponential decay of gradient reversely in time. The gradient decay is first illustrated on intensive experiments on the standard sunspot series. The ability of the model to internally encode useful information on the underlying process is then illustrated by several experiments on well known chaotic processes. Parsimonious DRNN models are able to find an appropriate internal representation of various chaotic processes from the observation of a subset of the state variables.A. Aussem1999-10-08Z2011-03-11T08:54:03Zhttp://cogprints.org/id/eprint/550This item is in the repository with the URL: http://cogprints.org/id/eprint/5501999-10-08ZNeural-based Queueing System Modelling for Service Quality Estimation in B-ISDN NetworksThis paper addresses an original scheme based on feedforward neural networks, aimed at modelling queueing systems fed with bursty traffic. A neural network is trained to anticipate the average number of waiting cells, the cell loss rate and the coefficient of variation of the cell inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the cell inter-arrival time. Our long-term goal is the design of a preventive control strategy in B-ISDN networks based on distributed neural networks modelling each queueing system located at the input and output ports of the switching facilities. To illustrate the potential of neural networks for modelling queueing systems, a neural network is successfully trained to model OnOff/D/1/c, OnOff/OnOff/1/c and multi-OnOff/D/1/c queueing systems.Alex AussemSebastien RouxelRaymond Marie1999-09-29Z2011-03-11T08:54:20Zhttp://cogprints.org/id/eprint/833This item is in the repository with the URL: http://cogprints.org/id/eprint/8331999-09-29ZSens et temps de la Gestalt (Gestalt theory: critical overview and contemporary relevance)Rather than mere psychological doctrine, Gestalt theory was conceived of as a general theory of form and organization deemed to lay a unified groundwork for several domains of scientific endeavor. Our aim in this article is to assess the legacy of this framework, and examine its relevance for present-day research in cognitive science. We thus survey the intellectual contexts within which Gestalt theory originated and evolved, and review some of its central features: a phenomenological approach to philosophy and science; grounding in the field theory of physics and in the theory of dynamical systems in mathematics; perception viewed as a general structure of cognition; intrinsic interrelatedness of forms and values; unitary approach to perceiving, acting, and expression. We hope this review will allow for a clarification of the status of Gestalt concepts in cognitive and language sciences, both with respect to fields of inquiry wherein they continue to exert substantial influence as well as in regard to fields from which all reference to Gestalt ideas has long since disappeared. We submit that the legacy of Gestalt theory will be most usefully reappraised with respect to its dynamic principles, although this reappraisal would entail a critical examination of the customary Gestalt concepts of time and psychogenesis, as well as a reconsideration of the status of motion and action in form (and/or meaning) constitution.Victor RosenthalYves-Marie Visetti2000-02-09Z2011-03-11T08:53:41Zhttp://cogprints.org/id/eprint/139This item is in the repository with the URL: http://cogprints.org/id/eprint/1392000-02-09ZThe theory of the organism-environment system: III. Role of efferent influences on receptors in the formation of knowledge.The present article is an attempt to give - in the frame of the theory of the organism-environment system (Jarvilehto 1998a) - a new interpretation to the role of efferent influences on receptor activity and to the functions of senses in the formation of knowledge. It is argued, on the basis of experimental evidence and theoretical considerations, that the senses are not transmitters of environmental information, but they create a direct connection between the organism and the environment, which makes the development of a dynamic living system, the organism-environment system, possible. In this connection process the efferent influences on receptor activity are of particular significance, because with their help the receptors may be adjusted in relation to the parts of the environment which are most important in the achievement of behavioral results. Perception is the process of joining of new parts of the environment to the organism-environment system; thus, the formation of knowledge by perception is based on reorganization (widening and differentiation) of the organism-environment system, and not on transmission of information from the environment. With the help of the efferent influences on receptors each organism creates its own peculiar world which is simultaneously subjective and objective. The present considerations have far reaching influences as well on experimental work in neurophysiology and psychology of perception as on philosophical considerations of knowledge formation.Timo Jarvilehto2000-06-15Z2011-03-11T08:53:53Zhttp://cogprints.org/id/eprint/408This item is in the repository with the URL: http://cogprints.org/id/eprint/4082000-06-15ZWeaving, Bending, Patching, Mending the Fabric of Reality: A Cognitive Science Perspective on Worldview InconsistencyIn order to become aware of inconsistencies, one must first construe of the world in a way that reflects its consistencies. This paper begins with a tentative model for how a set of discrete memories transforms into an interconnected worldview wherein relationships between memories are forged by way of abstractions. Inconsistencies prompt the invention of new abstractions. In regions of the conceptual network where inconsistencies abound, a cognitive analog of simulated annealing is in order; there is a willingness to question previous assumptionsto loosen conceptual relationshipsso as to let new concepts thoroughly percolate through the worldview and exert the needed revolutionary effect. In so doing there is a risk of assimilating dangerous concepts. Repression arrests the process by which dangerous thoughts infiltrate the conceptual network, and deception blocks thoughts that have already been assimilated. These forms of self-initiated worldview inconsistency may evoke feelings of fragmentation at the level of the individual or the society.Liane M. Gabora1999-10-08Z2011-03-11T08:54:03Zhttp://cogprints.org/id/eprint/549This item is in the repository with the URL: http://cogprints.org/id/eprint/5491999-10-08ZWedding connectionist and algorithmic modelling towards forecasting Caulerpa taxifolia development in the north-western Mediterranean seaWe discuss the use of supervised neural networks as a metamodelling technique for discrete event stochastic simulation in order to reduce significantly the computational burden involved by discrete simulations. A sophisticated computer model, coupling a Geographical Information System with a stochastic discrete event simulator, has been developed to anticipate the propagation of the green alga {\em Caulerpa taxifolia} in the North-Western Mediterranean sea. The simulation model provides reliable predictions, a couple of years in advance, of: i) the local expansion patterns of the alga, ii) the increase of {\em C. taxifolia} biomass and iii), the covered surfaces. However because the algorithmic model accounts for spatial interactions and anthropic dispersion/activities such as eradication, introduction of specific predators etc., simulations are extremely time and memory consuming. Therefore, to reduce the computational burden, a neural network was successfully trained on artificially generated data provided by the simulation runs to provide accurate forecasts 12 years in advance along with associated confidence intervals. The ability of the neural networks to capture the underlying physics of the phenomena is clearly illustrated by several preliminary experiments on a large coastal area. The neural network is able to construct, on this site, estimates of the {\em Caulerpa taxifolia} expansion 12 years in advance in good agreement with the simulation trajectories.Alex AussemDavid. Hill1998-08-06Z2011-03-11T08:53:38Zhttp://cogprints.org/id/eprint/57This item is in the repository with the URL: http://cogprints.org/id/eprint/571998-08-06ZFrom Neurons to Brain: Adaptive Self-Wiring of NeuronsDuring embryonic morpho-genesis, a collection of individual neurons turns into a functioning network with unique capabilities. Only recently has this most staggering example of emergent process in the natural world, began to be studied. Here we propose a navigational strategy for neurites growth cones, based on sophisticated chemical signaling. We further propose that the embryonic environment (the neurons and the glia cells) acts as an excitable media in which concentric and spiral chemical waves are formed. Together with the navigation strategy, the chemical waves provide a mechanism for communication, regulation, and control required for the adaptive self-wiring of neurons.Ronen SegevEshel Ben-Jacob1998-11-10Z2011-03-11T08:54:01Zhttp://cogprints.org/id/eprint/519This item is in the repository with the URL: http://cogprints.org/id/eprint/5191998-11-10ZDouble Loops Flows and Bidirectional Hebb's Law in Neural NetworkThis paper presents the double loop feedback model, which is used for structure and data flow modelling through reinforcement learning in an artificial neural network. We first consider physiological arguments suggesting that loops and double loops are widely spread in the exchange flows of the central nervous system. We then demonstrate that the double loop pattern, named a mental object, works as a functional memory unit and we describe the main properties of a double loop resonator built with the classical Hebb's law learning principle in a feedforward basis. In this model, we show how some mental objects aggregate themselves in building blocks, then what are the properties of such blocks. We propose the mental objects block as the representing structure of a concept in a neural network. We show how the local application of Hebb's law at the cell level leads to the concept of functional organization cost at the network level (upward effect), which explains spontaneous reorganization of mental blocks (downward effect). In this model, the simple hebbian learning paradigm appears to have emergent effects in both upward and downward directions.Christophe Lecerf1998-06-24Z2011-03-11T08:53:44Zhttp://cogprints.org/id/eprint/205This item is in the repository with the URL: http://cogprints.org/id/eprint/2051998-06-24ZThe evolution of a lexicon and meaning in robotic agents through self-organizationThis paper discusses interdisciplinary experiments, combining robotics and evolutionary computational linguistics. The goal of the experiments is to investigate if robotic agents can originate a language, in particular a lexicon. In the experiments two robots engage in a series of so-called language games. Starting from the assumption that the robots know how to communicate and are able to detect some sensory information from the environment, the agents ground conceptual meaning and develop a lexicon. The experiments show that the robots are able to form a shared communication system. The paper investigates the influence of using non-linguistic information in the formation of the lexicon, which takes the form of pointing (1) to indicate the topic of the language game, and (2) to give feedback on the outcome of the game.Paul Vogt1998-05-22Z2011-03-11T08:54:10Zhttp://cogprints.org/id/eprint/665This item is in the repository with the URL: http://cogprints.org/id/eprint/6651998-05-22ZModels of Speaking (To Their Amazement) Meet Speech-Synchronized GesturesThe chapters in this volume have generally accepted the argument that speech-gesture integration is basic to language use. But what explains the integration itself? I will attempt to make the case that it can be understood with the concept of a `growth point' or GP (McNeill & Duncan this volume) It is called a GP since it is a theoretical unit in which principles that explain mental growth -- differentiation, internalization, dialectic, and reorganization -- apply to realtime utterance generation by adults (and children). It is also called a GP since it is meant to be the initial form of a thinking-while-speaking unit out of which a dynamic process of organization emerges. The emergence unpacks the GP into a surface utterance and gesture that articulates its meaning implications.David McNeill2000-08-02Z2011-03-11T08:54:22Zhttp://cogprints.org/id/eprint/915This item is in the repository with the URL: http://cogprints.org/id/eprint/9152000-08-02ZNeural implementation of psychological spacesPsychological spaces give natural framework for construction of mental representations. Neural model of psychological spaces provides a link between neuroscience and psychology. Categorization performed in high-dimensional spaces by dynamical associative memory models is approximated with low-dimensional feedforward neural models calculating probability density functions in psychological spaces. Applications to the human categorization experiments are discussed.
Wlodzislaw Duch1998-06-28Z2011-03-11T08:53:49Zhttp://cogprints.org/id/eprint/338This item is in the repository with the URL: http://cogprints.org/id/eprint/3381998-06-28ZLe secret de la chambre chinoiseIt is shown, both deductively and with the help of the empirical evidence provided by Libet, that consciousness is deprived of any decisional power. Consciousness' role is confined to transmitting instructions to the body as a function of the affect both generated and evoked by perception. The existence of language allows human subjects to produce a self-justification discourse about their own thoughts and deeds. Such discourse does not reflect accurately however the psychological mechanisms effectively at work. The impact of consciousness consists only in influencing both the affect of the speaker him/herself (whether under the form of proper speech or under that of "inner speech"), and the affect of any hearer. The "body" and "soul" pair is thus vindicated but the responsibilities traditionally assigned to each need to be reformulated, contrasting on the one hand a body making decisions and acting accordingly, and a soul confined to retro-acting and at the level of the affect only.Paul Jorion1998-07-27Z2011-03-11T08:54:15Zhttp://cogprints.org/id/eprint/731This item is in the repository with the URL: http://cogprints.org/id/eprint/7311998-07-27ZThe Engine of Awareness: Autonomous Synchronous RepresentationsObjections to functional explanations of awareness assert that although functional systems may be adequate to explain behavior, including verbal behavior consisting of assertions of awareness by an individual, they cannot provide for the existence of phenomenal awareness. In this paper, a theory of awareness is proposed that counters this assertion by incorporating two advances: (1) a formal definition of representation, expressed in a functional notation: Newell's Representation Law, and 2) the introduction of real time into the analysis of awareness. This leads to the definition of phenomenal awareness as existing whenever an object contains an autonomously updated configuration satisfying the Representation Law with respect to some aspects of its environment. The relational aspect of the Representation Law permits the development of multiple levels of awareness, which provides for the existence of illusions and hallucinations, and permits the identification of a new measure, accuracy of awareness . The relational perspective also permits the incorporation of referential concepts into the framework. Qualia can then be identified with referentially opaque elements of awareness. The functional form of the Representation Law is linked to neurophysiology and the underlying phenomena of chemistry and physics by phenomena involving activity-dependent connectivity.George McKee1999-01-27Z2011-03-11T08:54:17Zhttp://cogprints.org/id/eprint/794This item is in the repository with the URL: http://cogprints.org/id/eprint/7941999-01-27ZThe Origin and Evolution of Culture and CreativityLike the information patterns that evolve through biological processes, mental representations, or memes, evolve through adaptive exploration and transformation of an information space through variation, selection, and transmission. Since unlike genes, memes do not come packaged with instructions for their replication, our brains do it for them, strategically, guided by a fitness landscape that reflects both internal drives and a worldview that is continually updated through meme assimilation. This paper presents a model for how an individual becomes a meme-evolving agent via the emergence of an autocatalytic network of sparse, distributed memories, and discusses implications for complex, creative thought processes and why they are unique to humans. Memetics can do more than account for the spread of catchy tunes; it can pave the way for the kind of overarching framework for the humanities that the first form of evolution has provided for the biological sciences.L. Gabora2001-03-31Z2011-03-11T08:54:37Zhttp://cogprints.org/id/eprint/1425This item is in the repository with the URL: http://cogprints.org/id/eprint/14252001-03-31ZApplications of nonlinear dynamic systems theory in developmental psychology: Motor and cognitive developmentApplications of nonlinear dynamical systems theory to psychology have led to recent advances in understanding neuromotor development and advances in theories of cognitive development. This article reviews published findings associated with a specific coherent and influential application from which a theory of adaptive, self-organized cognition has been derived and related to a theory of developmental dynamics of the neuromotor system. The review focuses on implications of two theories for quantifying developmental phenomena, and suggests a method for quantifying the cognitive theory.Mary Ann Metzger2003-02-10Z2011-03-11T08:55:09Zhttp://cogprints.org/id/eprint/2764This item is in the repository with the URL: http://cogprints.org/id/eprint/27642003-02-10ZTowards a unified model of cortical computation II: From control architecture to a model of consciousnessThe recently introduced Static and Dynamic State (SDS)
Feedback control scheme together with its modified form, the Data Compression and Reconstruction (DCR) architecture that performs pseudoinverse computation, suggests a unified model of cortical processing including consciousness. The constraints of the model are outlined were and the features of the cortical architecture that are suggested and sometimes dictated by these constraints are listed. Constraints are imposed on cortical layers, e.g., (1) the model prescribes a connectivity substructure that is shown to fit the main properties of the `basic neural circuit' of the cerebral cortex (Shepherd and Koch, 1990, Douglas and Martin 1990). In: The synaptic organization of the brain, Oxford University Press, 1990), and (2) the stability requirements of the pseudoinverse method offer an explanation for the columnar organization of the cortex. Constraints are also imposed on the hierarchy of cortical areas, e.g., the proposed control architecture requires computations of the control variables belonging to both the `desired' and the experienced' moves as well as a `sign-proper' separation of feedback channels that fit known properties of the basal ganglia -- thalamocortical loops (Lorincz, 1997). An outline is given as to how the DCR scheme can be extended towards a model for consciousness that can deal with the `homunculus fallacy' by resolving the fallacy and saving he homunculus as an inherited and learnt partially ordered list of preferences.Andras Lorinczandras.lorincz1999-10-08Z2011-03-11T08:54:03Zhttp://cogprints.org/id/eprint/551This item is in the repository with the URL: http://cogprints.org/id/eprint/5511999-10-08ZCombining Neural Network Forecasts on Wavelet-Transformed Time SeriesWe discuss a simple strategy aimed at improving neural network prediction accuracy, based on the combination of predictions at varying resolution levels of the domain under investigation (here: time series). First, a wavelet transform is used to decompose the time series into varying scales of temporal resolution. The latter provide a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Then, a Dynamical Recurrent Neural Network (DRNN) is trained on each resolution scale with the temporal-recurrent backpropagation (TRBP) algorithm. By virtue of its internal dynamic, this general class of dynamic connectionist network approximates the underlying law governing each resolution level by a system of nonlinear difference equations. The individual wavelet scale forecasts are afterwards recombined to form the current estimate. The predictive ability of this strategy is assessed with the sunspot series.Alex AussemFionn Murtagh1998-06-16Z2011-03-11T08:53:49Zhttp://cogprints.org/id/eprint/325This item is in the repository with the URL: http://cogprints.org/id/eprint/3251998-06-16ZComputational and dynamical models of mindVan Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt Governor to fulfill the role of a dynamicist Turing Machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt Governor is to be the theoretical exemplar of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt Governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism prevent him from providing a convincing alternative to current cognitive theories.Chris Eliasmith1998-06-24Z2011-03-11T08:53:59Zhttp://cogprints.org/id/eprint/479This item is in the repository with the URL: http://cogprints.org/id/eprint/4791998-06-24ZPerceptual grounding in robotsThis paper reports on an experiment in which robotic agents are able to ground objects in their environment using low-level sensors. The reported experiment is part of a larger experiment, in which autonomous agents ground an adaptive language through self-organization. Grounding is achieved by the implementation of the hypothesis that meaning can be created using mechanisms like feature generation and self-organization. The experiments were carried out to investigate how agents may construct features in order to learn to discriminate objects from each other. Meaning is formed to give semantic value to the language, which is also created by the agents in the same experiments. From the experimental results we can conclude that the robots are able to ground meaning in this self-organizing manner. This paper focuses on the meaning creation and will only discuss the language formation very briefly. The paper sketches the tested hypothesis, the experimental set-up and experimental results.Paul Vogt1998-06-15Z2011-03-11T08:54:11Zhttp://cogprints.org/id/eprint/684This item is in the repository with the URL: http://cogprints.org/id/eprint/6841998-06-15ZA Rational Analysis of Alternating Search and Reflection Strategies in Problem SolvingIn this paper two approaches to problem solving, search and reflection, are discussed, and combined in two models, both based on rational analysis (Anderson, 1990). The first model is a dynamic growth model, which shows that alternating search and reflection is a rational strategy. The second model is a model in ACT-R, which can discover and revise strategies to solve simple problems. Both models exhibit the explore-insight pattern normally attributed to insight problem solving.Niels A. Taatgen1998-06-09Z2011-03-11T08:53:57Zhttp://cogprints.org/id/eprint/448This item is in the repository with the URL: http://cogprints.org/id/eprint/4481998-06-09ZAction Selection in a hypothetical house robot: Using those RL numbersReinforcement Learning (RL) methods, in contrast to many forms of machine learning, build up value functions for actions. That is, an agent not only knows `what' it wants to do, it also knows `how much' it wants to do it. Traditionally, the latter are used to produce the former and are then ignored, since the agent is assumed to act alone. But the latter numbers contain useful information - they tell us how much the agent will suffer if its action is not executed (perhaps not much). They tell us which actions the agent can compromise on and which it cannot. It is clear that many interesting systems possess multiple parallel and conflicting goals, all demanding attention, and none of which can be fully satisfied expect at the expense of others. Animals are the prime example of such systems. In [Humphrys, 1995], I introduced the W-learning algorithms, showing one method of resolving competition among behaviors automatically by reference to their RL values. The scheme has the unusal feature that behaviors are at all times in selfish pursuit of their own goals and have no explicit concept of cooperation, despite residing in the same body. In this paper, I apply W-learning to the world of a hypothetical house robot, which doubles as family toy, movile security camera, mobile smoke alarm and occasional vacuum cleaner. I show how a W-learning community of behaviors inside the robot will support a robust behavior pattern, capabable of opportunistic behavior, avoiding dithering, and allowing for the concept of default behavior and expression of low-priority goals.Mark Humphrys1998-06-09Z2011-03-11T08:53:57Zhttp://cogprints.org/id/eprint/447This item is in the repository with the URL: http://cogprints.org/id/eprint/4471998-06-09ZAction Selection methods using Reinforcement LearningAction Selection schemes, when translated into precise algorithms, typically involve considerable design effort and tuning of parameters. Little work has been done on solving the problem using learning. This paper compares eight different methods of solving the action selection problem using Reinforcement Learning (learning from rewards). The methods range from centralised and cooperative to decentralised and selfish. They are tested in an artificial world and their performance, memory requirements and reactiveness are compared. Finally, the possibility of more exotic, ecosystem-like decentralised models are considered.Mark Humphrys2000-08-02Z2011-03-11T08:54:22Zhttp://cogprints.org/id/eprint/914This item is in the repository with the URL: http://cogprints.org/id/eprint/9142000-08-02ZComputational physics of the mindIn the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures. Wlodzislaw Duch1998-06-15Z2011-03-11T08:53:42Zhttp://cogprints.org/id/eprint/169This item is in the repository with the URL: http://cogprints.org/id/eprint/1691998-06-15ZThe Dilemma of Saussurean CommunicationA Saussurean communication system exists when an entire communicating population uses a single "language" that maps states unambiguously onto symbols and then back into the original states. This paper describes a number of simulations performed with a genetic algorithm to investigate the conditions necessary for such communication systems to evolve. The first simulation shows that Saussurean communication evolves in the simple case where direct selective pressure is placed on individuals to be both good transmitters and good receivers. The second simulation demonstrates that, in the more realistic case where selective pressure is only placed on doing well as a receiver, Saussurean communication fails to evolve. Two methods, inspired by research on the Prisoner's Dilemma, are used to attempt to solve this problem. The third simulation shows that, even in the absence of selective pressure on transmission, Saussurean communication can evolve if individuals interact multiple times with the same communication partner and are given the ability to respond differentially based on past interaction. In the fourth simulation, spatially organized populations are used, and it is shown that this allows Saussurean communication to evolve through kin selection.Michael Oliphant1998-06-16Z2011-03-11T08:53:49Zhttp://cogprints.org/id/eprint/324This item is in the repository with the URL: http://cogprints.org/id/eprint/3241998-06-16ZThe third contender: A critical examination of the dynamicist theory of cognitionIn a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. In this paper I begin with a short description of the dynamicist project and its role as a cognitive theory. Subsequently, I determine the theoretical commitments of dynamicists, critically examine those commitments and discuss current examples of dynamicist models. In conclusion, I determine dynamicism's relation to symbolicism and connectionism and find that the dynamicist goal to establish a new paradigm has yet to be realized.Chris Eliasmith2006-05-25Z2011-03-11T08:56:26Zhttp://cogprints.org/id/eprint/4888This item is in the repository with the URL: http://cogprints.org/id/eprint/48882006-05-25ZThe Cognitive Mechanisms Guiding Psychological DevelopmentThis Thesis presents a model of cognitive development
inspired by Piaget's "Genetic Epistemology". It is observed that the epigenetic process described by Piaget posess mechanisms and behaviour that characterise complex adaptive systems. A model of bipedal motion based around the "Bucket Brigade" algorithm of Holland is presened to explore this relationship.
G Osborne64061998-06-09Z2011-03-11T08:53:58Zhttp://cogprints.org/id/eprint/452This item is in the repository with the URL: http://cogprints.org/id/eprint/4521998-06-09ZW-learning: Competition among selfish Q-learnersW-learning is a self-organising action-selection scheme for systems with multiple parallel goals, such as autonomous mobile robots. It uses ideas drawn from the subsumption architecture for mobile robots (Brooks), implementing them with the Q-learning algorithm from reinforcement learning (Watkins). Brooks explores the idea of multiple sensing-and-acting agents within a single robot, more than one of which is capable of controlling the robot on its own if allowed. I introduce a model where the agents are not only autonomous, but are in fact engaged in direct competition with each other for control of the robot. Interesting robots are ones where no agent achieves total victory, but rather the state-space is fragmented among different agents. Having the agents operate by Q-learning proves to be a way to implement this, leading to a local, incremental algorithm (W-learning) to resolve competition. I present a sketch proof that this algorithm converges when the world is a discrete, finite Markov decision process. For each state, competition is resolved with the most likely winner of the state being the agent that is most likely to suffer the most if it does not win. In this way, W-learning can be viewed as `fair' resolution of competition. In the empirical section, I show how W-learning may be used to define spaces of agent-collections whose action selection is learnt rather than hand-designed. This is the kind of solution-space that may be searched with a genetic algorithm.Mark Humphrys2001-05-09Z2011-03-11T08:54:38Zhttp://cogprints.org/id/eprint/1486This item is in the repository with the URL: http://cogprints.org/id/eprint/14862001-05-09ZBinding in Models of Perception and Brain
FunctionThe development of the issue of binding as fundamental to neural dynamics has made possible recent advances in the modeling of difficult problems of perception and brain function. Among them is perceptual segmentation, invariant pattern recognition and one-shot learning. Also, longer-term conceptual developments that have led to this success are reviewed.Christoph von der Malsburg1999-10-06Z2011-03-11T08:54:03Zhttp://cogprints.org/id/eprint/548This item is in the repository with the URL: http://cogprints.org/id/eprint/5481999-10-06ZDynamical Recurrent Neural Networks: Towards Environmental Time Series Prediction}Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed {\em nowcasting} (Murtagh et al.\ 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy {\em k}-nearest neighbors method.A. AussemF. MurtaghM. Sarazin2001-05-09Z2011-03-11T08:54:38Zhttp://cogprints.org/id/eprint/1485This item is in the repository with the URL: http://cogprints.org/id/eprint/14852001-05-09ZFace Recognition and Gender DeterminationThe system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces.Laurenz WiskottJean-Marc FellousNorbert KrügerChristoph von der Malsburg2001-06-19Z2011-03-11T08:54:41Zhttp://cogprints.org/id/eprint/1593This item is in the repository with the URL: http://cogprints.org/id/eprint/15932001-06-19ZGrounding symbols in sensorimotor categories with neural networksIt is unlikely that the systematic, compositional properties of formal symbol systems -- i.e., of
computation -- play no role at all in cognition. However, it is equally unlikely that cognition is just
computation, because of the symbol grounding problem (Harnad 1990): The symbols in a symbol
system are systematically interpretable, by external interpreters, as meaning something, and that is a
remarkable and powerful property of symbol systems. Cognition (i.e., thinking), has this property too:
Our thoughts are systematically interpretable by external interpreters as meaning something. However,
unlike symbols in symbol systems, thoughts mean what they mean autonomously: Their meaning does
not consist of or depend on anyone making or being able to make any external interpretations of them
at all. When I think "the cat is on the mat," the meaning of that thought is autonomous; it does not
depend on YOUR being able to interpret it as meaning that (even though you could interpret it that
way, and you would be right).Stevan Harnad2001-06-19Z2011-03-11T08:54:41Zhttp://cogprints.org/id/eprint/1596This item is in the repository with the URL: http://cogprints.org/id/eprint/15962001-06-19ZLearned Categorical Perception in Neural Nets: Implications for Symbol GroundingAfter people learn to sort objects into categories they see them differently. Members of
the same category look more alike and members of different categories look more different. This
phenomenon of within-category compression and between-category separation in similarity space is
called categorical perception (CP). It is exhibited by human subjects, animals and neural net models.
In backpropagation nets trained first to auto-associate 12 stimuli varying along a one-dimensional
continuum and then to sort them into 3 categories, CP arises as a natural side-effect because of four
factors: (1) Maximal interstimulus separation in hidden-unit space during auto-association learning, (2)
movement toward linear separability during categorization learning, (3) inverse-distance repulsive force
exerted by the between-category boundary, and (4) the modulating effects of input iconicity, especially
in interpolating CP to untrained regions of the continuum. Once similarity space has been "warped" in
this way, the compressed and separated "chunks" have symbolic labels which could then be combined
into symbol strings that constitute propositions about objects. The meanings of such symbolic
representations would be "grounded" in the system's capacity to pick out from their sensory
projections the object categories that the propositions were about. Stevan HarnadStephen J. HansonJoseph Lubin1999-01-22Z2011-03-11T08:54:02Zhttp://cogprints.org/id/eprint/531This item is in the repository with the URL: http://cogprints.org/id/eprint/5311999-01-22ZMeme and Variations: A Computational Model of Cultural EvolutionThis paper describes a computational model of how ideas, or memes, evolve through the processes of variation, selection, and replication. Every iteration, each neural-network based agent in an artificial society has the opportunity to acquire a new meme, either through 1) INNOVATION, by mutating a previously-learned meme, or 2) IMITATION, by copying a meme performed by a neighbor. Imitation, mental simulation, and using past experience to bias mutation all increase the rate at which fitter memes evolve. Memes at epistatic loci converged more slowly than memes at over- or underdominant loci. The higher the ratio of innovation to imitation, the greater the meme diversity, and the higher the fitness of the fittest meme. Optimization is fastest for the society as a whole with an innovation to imitation ratio of 2:1, but diversity is comprimized.L. Gabora1998-06-09Z2011-03-11T08:53:57Zhttp://cogprints.org/id/eprint/450This item is in the repository with the URL: http://cogprints.org/id/eprint/4501998-06-09ZTowards self-organising Action SelectionSystems with multiple parallel goals (e.g. autonomous mobile robots) have a problem analogous to that of action selection in ethology. Architectures such as the subsumption architecture (Brooks) involve multiple sensing-and-acting agents within a single robot on its own if allowed. Which to give control at a given moment is normally regarded as a (difficult) problem of design. In a quest for a scheme where the agents decide for themselves in a sensible manner, I introduce a model where the agents are not only autonomous but are in full competition with each other for control of the robot. Interesting robots are ones where no agent achieves total victory, but rather a serires of compromises are reached. Having the agents operate by the reinforcement learning algorithm Q-learning (Watkins) allows the introduction of an algorithm called `W-learning', by which the agents learn to focus their competitive efforts in a manner similar to agents with limited spending power in an economy. In this way, the population of agents organises its own action selection in a coherent way that supports parallelism and opportunism. In the empirical section, I show how the relative influence an agent has on its robot may be controlled by adjusting its rewards. The possibility of automated search of agent-combinations is considered.Mark Humphrys1998-06-09Z2011-03-11T08:53:57Zhttp://cogprints.org/id/eprint/451This item is in the repository with the URL: http://cogprints.org/id/eprint/4511998-06-09ZW-learning: A simple RL-based Society of MindW-learning is a self-organising action-selection scheme for systems with multiple parallel goals, such as autonomous mobile robots. It uses ideas drawn from the subsumption architecture for mobile robots (Brooks), implementing them with the Q-learning algorithm from reinforcement learning (Watkins). Brooks explores the idea of multiple sensing-and-acting agents within a single robot, more than one of which is capable of controlling the robot on its own if allowed. I introduce a model where the agents are not only autonomous, but are in fact engaged in direct competition with each other for control of the robot. Interesting robots are ones where no agent achieves total victory, but rather the state-space is fragmented among different agents. Having the agents operate by Q-learning proves to be a way to implement this, leading to a local, incremental algorithm (W-learning) to resolve competition. I present a sketch proof that this algorithm converges when the world is a discrete, finite Markov decision process. For each state, competition is resolved with the most likely winner of the state being the agent that is most likely to suffer the most if it does not win. In this way, W-learining can be viewed as `fair' resolution of competition. In the empirical section, I show how W-learning may be used to define spaces of agent-collections whose action selection is learnt rather than hand-designed. This is the kind of solution-space that may be searched with a genetic algorithm.Mark Humphrys2001-06-19Z2011-03-11T08:54:41Zhttp://cogprints.org/id/eprint/1594This item is in the repository with the URL: http://cogprints.org/id/eprint/15942001-06-19ZDoes the Mind Piggy-Back on Robotic and Symbolic Capacity? Cognitive science is a form of "reverse engineering" (as Dennett has dubbed it). We are
trying to explain the mind by building (or explaining the functional principles of) systems that have
minds. A "Turing" hierarchy of empirical constraints can be applied to this task, from t1, toy models
that capture only an arbitrary fragment of our performance capacity, to T2, the standard "pen-pal"
Turing Test (total symbolic capacity), to T3, the Total Turing Test (total symbolic plus robotic
capacity), to T4 (T3 plus internal [neuromolecular] indistinguishability). All scientific theories are
underdetermined by data. What is the right level of empirical constraint for cognitive theory? I will
argue that T2 is underconstrained (because of the Symbol Grounding Problem and Searle's Chinese
Room Argument) and that T4 is overconstrained (because we don't know what neural data, if any, are
relevant). T3 is the level at which we solve the "other minds" problem in everyday life, the one at
which evolution operates (the Blind Watchmaker is no mind-reader either) and the one at which
symbol systems can be grounded in the robotic capacity to name and manipulate the objects their
symbols are about. I will illustrate this with a toy model for an important component of T3 --
categorization -- using neural nets that learn category invariance by "warping" similarity space the way
it is warped in human categorical perception: within-category similarities are amplified and
between-category similarities are attenuated. This analog "shape" constraint is the grounding inherited
by the arbitrarily shaped symbol that names the category and by all the symbol combinations it enters
into. No matter how tightly one constrains any such model, however, it will always be more
underdetermined than normal scientific and engineering theory. This will remain the ineliminable
legacy of the mind/body problem. Stevan Harnad2001-06-18Z2011-03-11T08:54:41Zhttp://cogprints.org/id/eprint/1586This item is in the repository with the URL: http://cogprints.org/id/eprint/15862001-06-18ZGrounding Symbols in the Analog World with Neural NetsHarnad's main argument can be roughly summarised as follows: due to Searle's
Chinese Room argument, symbol systems by themselves are insufficient to
exhibit cognition, because the symbols are not grounded in the real world, hence
without meaning. However, a symbol system that is connected to the real world
through transducers receiving sensory data, with neural nets translating these
data into sensory categories, would not be subject to the Chinese Room
argument.
Harnad's article is not only the starting point for the present debate, but is also a
contribution to a longlasting discussion about such questions as: Can a computer
think? If yes, would this be solely by virtue of its program? Is the Turing Test
appropriate for deciding whether a computer thinks?Stevan Harnad2002-10-18Z2011-03-11T08:55:04Zhttp://cogprints.org/id/eprint/2541This item is in the repository with the URL: http://cogprints.org/id/eprint/25412002-10-18ZL'Ancrage des Symboles dans le Monde Analogique a l'aide de Reseaux Neuronaux: un Modele HybrideLe modele d'ancrage propose ici est simple a recapituler. Les projections sensorielles analogiques sont les intrants des reseaux neuronaux qui doivent apprendre a connecter certaines des projections avec certains symboles (le nom de leur categorie) et certaines autres projections avec d'autres symboles (les noms d'autres categories pouvant se confondre les unes aux autres), en trouvant et en utilisant les invariants qui les representent de facon a favoriser l'accomplissement d'une categorisation juste. Les symboles ancres sont alors enfiles dans des combinaisons d'ordre superieur (descriptions symboliques ancrees) par un deuxieme processus combinatoire qui presente une difference critique a l'egard de la manipulation symbolique classique. Dans la manipulation symbolique standard (non ancree), la syntaxe est la seule contrainte a laquelle les combinaisons de symboles sont soumises et elle s'applique a la configuration (arbitraire) des symboles. Dans un systeme symbolique ancre, on doit tenir compte d'une deuxieme contrainte, celle de la forme non arbitraire des invariants sensoriels qui connectent le symbole a la projection sensorielle analogique de l'objet auquel il se rapporte. Je ne peux m'etendre sur la nature de ces systemes symboliques ancres a double contrainte , si ce n'est que pour indiquer que la perception categorielle humaine peut apporter quelques indices quant a la nature de cette interaction entre les contraintes analogiques et syntaxiques.Stevan Harnad2001-06-18Z2011-03-11T08:54:41Zhttp://cogprints.org/id/eprint/1588This item is in the repository with the URL: http://cogprints.org/id/eprint/15882001-06-18ZSymbol Grounding is an Empirical Problem: Neural Nets are Just a Candidate Component"Symbol Grounding" is beginning to mean too many things to too many people. My own construal has always
been simple: Cognition cannot be just computation, because computation is just the systematically interpretable manipulation of
meaningless symbols, whereas the meanings of my thoughts don't depend on their interpretability or interpretation by someone
else. On pain of infinite regress, then, symbol meanings must be grounded in something other than just their interpretability if they
are to be candidates for what is going on in our heads. Neural nets may be one way to ground the names of concrete objects and
events in the capacity to categorize them (by learning the invariants in their sensorimotor projections). These grounded
elementary symbols could then be combined into symbol strings expressing propositions about more abstract categories.
Grounding does not equal meaning, however, and does not solve any philosophical problems. Stevan Harnad1998-06-26Z2011-03-11T08:53:59Zhttp://cogprints.org/id/eprint/482This item is in the repository with the URL: http://cogprints.org/id/eprint/4821998-06-26ZParallel Processing of Graph Reachability in DatabasesOne of the features that distinguishes digital libraries from traditional databases is new cost models for client-access to intellectual property. Clients will pay for accessing data items in digital libraries, and we believe that optimizing these costs will be as important as optimizing performance in traditional databases. In this paper we discuss cost models and protocols for accessing digital libraries, with the objective of determining the minimum cost protocol for each model. We expect that in the future information appliances will come equipped with a cost optimizer, in the same way that today computers come with a built-in operating system. This paper makes the initial steps towards a theory and practice of intellectual property cost management.W. Zhang O. Wolfson1999-06-15Z2011-03-11T08:53:40Zhttp://cogprints.org/id/eprint/99This item is in the repository with the URL: http://cogprints.org/id/eprint/991999-06-15ZGeneric mesoscopic neural networks based on statistical mechanics of neocortical interactionsA series of papers over the past decade [the most recent being L. Ingber, Phys. Rev. A 44, 4017 (1991)] has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions, demonstrating its capability in describing large-scale properties of short-term memory and electroencephalographic systematics. This methodology also defines an algorithm to construct a mesoscopic neural network, based on realistic neocortical processes and parameters, to record patterns of brain activity and to compute the evolution of this system. Furthermore, this algorithm is quite generic, and can be used to similarly process information in other systems, especially, but not limited to, those amenable to modeling by mathematical physics techniques alternatively described by path-integral Lagrangians, Fokker-Planck equations, or Langevin rate equations. This methodology is made possible and practical by a confluence of techniques drawn from SMNI itself, modern methods of functional stochastic calculus defining nonlinear Lagrangians, very fast simulated reannealing, and parallel-processing computation.Lester Ingber1998-03-20Z2011-03-11T08:54:07Zhttp://cogprints.org/id/eprint/615This item is in the repository with the URL: http://cogprints.org/id/eprint/6151998-03-20ZThe Symbol Grounding ProblemThere has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem: How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and (2) categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., An X is a Y that is Z). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded.Stevan Harnad2003-08-12Z2011-03-11T08:55:19Zhttp://cogprints.org/id/eprint/3106This item is in the repository with the URL: http://cogprints.org/id/eprint/31062003-08-12ZThe Symbol Grounding ProblemThere has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem: How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and (2) categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., An X is a Y that is Z). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded.Stevan Harnad1998-06-25Z2011-03-11T08:53:59Zhttp://cogprints.org/id/eprint/480This item is in the repository with the URL: http://cogprints.org/id/eprint/4801998-06-25ZAn alternative neural network representation for conceptual knowledgeThis paper introduces the P-Graph representation of a neural network as an alternative to the classical « semantic networks » introduced in knowledge representation by Quillian. None of the shortcomings of Quillian-type semantic networks are displayed by it. The P-Graph is a particular type of dual of a graph: memory traces (typically words) are associated with the edges of the graph, the relations between the memory traces, with the vertices. The P-Graph is the mathematical object underlying ANELLA (Associative Network with Emergent Logical and Learning Abilities). The P-Graph in particular the way it grows - is shown to be compatible with the architecture of an actual biological neural network, its emergent logical and learning abilities are shown on examples borrowed from the working of ANELLA as developed at British Telecom Laboratories in 1988 under a BT Academic Fellowship.Paul Jorion1998-06-01Z2011-03-11T08:53:48Zhttp://cogprints.org/id/eprint/296This item is in the repository with the URL: http://cogprints.org/id/eprint/2961998-06-01ZThe frame of reference problem in cognitive modelingSince at least the mid-70's there has been widespread agreement among cognitive science researchers that models of a problem-solving agent should incorporate its knowledge about the world and an inference procedure for interpreting this knowledge to construct plans and take actions. Research questions have focused on how knowledge is represented in computer programs and how such cognitive models can be verified in psychological experiments. We are now experiencing increasing confusion and misunderstanding as different critiques are leveled against this methodology and new jargon is introduced (e.g., "not rules," "ready-to-hand," "background," "situated," "subsymbolic"). Such divergent approaches put a premium on improving our understanding of past modeling methods, allowing us to more sharply contrast proposed alternatives. This paper compares and synthesizes new robotic research that is founded on the idea that knowledge does not consist of objective representations of the world. This research develops a new view of planning that distinguishes between a robot designer's ontological preconceptions, the dynamics of a robot's interaction with an environment, and an observer's descriptive theories of patterns in the robot's behavior. These frame-of-reference problems are illustrated here and unified by a new framework for describing cognitive models.William J. Clancey1998-06-15Z2011-03-11T08:53:43Zhttp://cogprints.org/id/eprint/197This item is in the repository with the URL: http://cogprints.org/id/eprint/1971998-06-15ZRethinking the language bottleneck: Why don't animals learn to communicate?While most work on the evolution of language has been centered on the evolution of syntax, my focus in this paper is instead on more basic features that separate human communication from the systems of communication used by other animals. In particular, I argue that human language is the only existing system of learned arbitrary reference. While innate communication systems are, by definition, directly transmitted genetically, the transmission of a learned learned systems must be indirect. Learners must acquire the system by being exposed its the use in the community. Although it is reasonable that a learner has access to the utterances that are produced, it is less clear how accessible the meaning is that the utterance is intended to convey. This particularly problematic if the system of communication is symbolic -- where form and meaning are linked in a purely conventional way. Given this, I propose that the ability to transmit a learned symbolic system of communication from one generation to the next represents a key milestone in the evolution of language.Michael Oliphant1998-06-15Z2011-03-11T08:53:43Zhttp://cogprints.org/id/eprint/196This item is in the repository with the URL: http://cogprints.org/id/eprint/1961998-06-15ZThe learning barrier: Moving from innate to learned systems of communicationHuman language is a unique ability. It sits apart from other systems of communication in two striking ways: it is syntactic, and it is learned. While most approaches to the evolution of language have focused on the evolution of syntax, this paper explores the computational issues that arise in shifting from a simple innate communication system to an equally simple one that is learned. Associative network learning within an observational learning paradigm is used to explore the computational difficulties involved in establishing and maintaining a simple learned communication system. Because Hebbian learning is found to be sufficient for this task, it is proposed that the basic computational demands of learning are unlikely to account for the rarity of even simple learned communication systems. Instead, it is the problem of *observing* that is likely to be central -- in particular the problem of determining what meaning a signal is intended to convey.Michael Oliphant2004-09-07Z2011-03-11T08:55:41Zhttp://cogprints.org/id/eprint/3810This item is in the repository with the URL: http://cogprints.org/id/eprint/38102004-09-07ZQuantum Interactomics and Cancer Molecular Mechanisms Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.
*Communicated to: The Institute of Genomic Biology (currently under construction at UIUC, at 905 S. Goodwin Avenue, Urbana,IL.61801,USA).
KEYWORDS: Cancer cell interactomics; Somatic cell genomics and
Proteomics; current limitations of modular models of carcinogenesis;
Complex quantum dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments.
Dr. I.C. Baianuicb2004-07-06Z2011-03-11T08:55:37Zhttp://cogprints.org/id/eprint/3675This item is in the repository with the URL: http://cogprints.org/id/eprint/36752004-07-06ZNATURAL TRANSFORMATION MODELS IN MOLECULAR BIOLOGYMolecular models in terms of Categories, Functors and Natural Transformations are introduced for unimolecular chemical transformations, multi-molecular chemical and biochemical transformations. Novel approaches to realization of Relational Biology Models of Complex System Biology are introduced in terms of Natural Transformations between Functors of Molecular Categories. Several applications of such natural transformations are then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Other possible realizations in Molecular Biology and Relational Biology of Organisms are also suggested. Future developments will include: Fuzzy Relations in Biology; Categories of Lukasiewicz Logic Algebras and Intuitionistic Logic Algebras for Modeling of Complex Neural Network Processes; Stochastic, Genetic Networks in Lukn-Algebras, and Relational Biology Models of Complex Hormonal Controls. Professor Ion Baianuicb2004-10-06Z2011-03-11T08:55:42Zhttp://cogprints.org/id/eprint/3829This item is in the repository with the URL: http://cogprints.org/id/eprint/38292004-10-06ZNATURAL TRANSFORMATIONS OF MULTI-LEVEL ORGANISMAL STRUCTURES REPRESENTED AS ORGANISMIC SUPERCATEGORIES:
I. Generation of Categorical Limits and Colimits during Biological Development and Evolution A current update of our original 1980 publication entitled "Natural Transformations of Organismic Structures" is here presented, along with the original (1980) article. A unifying approach to the realization of Relational Biology models and Complex System Biology was reported in 1980 for the first time in terms of Natural Transformations between Functors of Organismic Supercategories and their generating categorical diagrams. The representation of organismal structures in terms of Organismic-Supercategories, Functors and their Natural Transformations was introduced for the investigation of developmental and evolutionary processes. Several applications of such natural transformations were presented in relation to embryogenesis and evolutionary processes involving natural selection and the emergence of 'optimally designed' organismal structures. Other molecular realizations in Relational Biology and the underlying Molecular Biology of organisms were also discussed. Current developments of this approach to Complex Systems Biology include: Fuzzy Relations in Biological Dynamics and Structural Biology, Categories of Lukasiewicz Logic Algebras as representations of Functional Genomics and Cell Interactomics, and Intuitionistic Logic Algebras in Topoi and Higher-Dimensional Algebras as possible models of the emergence of Human Consciousness through 'long-range' correlations and partially coherent, multi-level Neural Network processes.Dr. Ion Baianuicb1999-06-15Z2011-03-11T08:53:40Zhttp://cogprints.org/id/eprint/100This item is in the repository with the URL: http://cogprints.org/id/eprint/1001999-06-15ZStatistical mechanics of neocortical interactions. I. Basic formulationAn approach to collective aspects of the neocortical system is formulated by methods of modern nonlinear nonequilibrium statistical mechanics. Microscopic neuronal synaptic interactions, consistent with anatomical observations, are first spatially averaged over columnar domains. These spatially ordered domains retain contact with the original physical synaptic parameters, are consistent with observed columnar physiology, and are a suitable substrate for macroscopic spatial-temporal regions described by a Lagrangian formalism. Long-ranged influences from extrinsic and inter-regional afferents drive these short-ranged interactions, giving rise to several columnar mechanisms affecting macroscopic activity.Lester Ingber2001-05-09Z2011-03-11T08:54:36Zhttp://cogprints.org/id/eprint/1380This item is in the repository with the URL: http://cogprints.org/id/eprint/13802001-05-09ZThe Correlation Theory of Brain FunctionA summary of brain theory is given so far as it is contained within the framework of Localization Theory. Diffculties of this "conventional theory" are traced back to a specific deficiency: there is no way to express relations between active cells (as for instance their representing parts of the same object). A new theory is proposed to cure this deficiency. It introduces a new kind of dynamical control, termed synaptic modulation, according to which synapses switch between a conducting and a non- conducting state. The dynamics of this variable is controlled on a fast time scale by correlations in the temporal fine structure of cellular signals. Furthermore, conventional synaptic plasticity is replaced by a refined version. Synaptic modulation and plasticity form the basis for short-term and long-term memory, respectively. Signal correlations, shaped by the variable network, express structure and relationships within objects. In particular, the figure-ground problem may be solved in this way. Synaptic modulation introduces flexibility into cerebral networks which is necessary to solve the invariance problem. Since momentarily useless connections are deactivated, interference between different memory traces can be reduced, and memory capacity increased, in comparison with conventional associative memory.Christoph von der Malsburg2011-12-16T00:58:34Z2011-12-16T00:58:34Zhttp://cogprints.org/id/eprint/7755This item is in the repository with the URL: http://cogprints.org/id/eprint/77552011-12-16T00:58:34ZNatural Transformations of Organismic StructuresThe mathematical structures underlying the theories of organismic sets, (M, R)-systems and molecular sets are shown to be transformed naturally within the theory of categories and functors. Their natural transformations allow the comparison of distinct entities, as well as the modelling of dynamics in “organismic” structures.Prof. Dr. I. C. Baianuibaianu@illinois.edu2004-10-06Z2011-03-11T08:55:42Zhttp://cogprints.org/id/eprint/3822This item is in the repository with the URL: http://cogprints.org/id/eprint/38222004-10-06ZSTRUCTURAL ORDER AND PARTIAL DISORDER IN BIOLOGICAL SYSTEMS:
Structural "Fuzziness" underlying All Biological FunctionsThe presence of structural order and partial disorder is discussed for several important biological molecules such as DNA, enzymes and proteins, as well as for cellular structures such as nerve myelin. The relationship between structural "fuzziness" and biological function is discussed
as an important aspect of biological complexity and biodynamics. The possible effects of partial disorder on the electron density of states in biological structures are predicted based on known quantum theoretical computations for lattices in solids. Important phenomena such as Anderson delocalization, Hall effect and quantum tunneling are predicted to affect biological function. Novel experiments are being proposed by pulsed lasers, pulsed/FT-NMR and optical/NIR spectroscopy to monitor the effects of structural partial disorder and "fuzziness" on biological function. Novel methods for computer analysis of paracrystalline lattices such as nerve myelin and oriented DNA fibers are also proposed based on molecular models that include partial disorder.
Prof. Dr. I.C. Baianuicb2004-10-06Z2011-03-11T08:55:41Zhttp://cogprints.org/id/eprint/3820This item is in the repository with the URL: http://cogprints.org/id/eprint/38202004-10-06ZSTRUCTURAL ORDER AND PARTIAL DISORDER IN BIOLOGICAL SYSTEMS:
STRUCTURAL "FUZZINESS" UNDERLYING BIOLOGICAL FUNCTIONThe presence of structural order and partial disorder is discussed for several important biological molecules such as DNA, enzymes and proteins, as well as for cellular structures such as nerve myelin. The relationship between structural "fuzziness" and biological function is discussed
as an important aspect of biological complexity and biodynamics. The possible effects of partial disorder on the electron density of states in biological structures is predicted based on known quantum theoretical computations for lattices in solids. Important phenomena such as Anderson delocalization, Hall effect and quantum tunneling are predicted to affect biological function. Novel experiments are being suggested by pulsed lasers, pulsed/FT-NMR and optical/NIR spectroscopy in order to monitor the effects of structural partial disorder and "fuzziness" on biological function. Novel methods for computer analysis of paracrystalline lattices such as nerve myelin and oriented DNA fibers are also being proposed based on molecular models that include partial disorder.
Dr. I.C. Baianuicb2011-12-16T00:58:07Z2011-12-16T00:58:07Zhttp://cogprints.org/id/eprint/7753This item is in the repository with the URL: http://cogprints.org/id/eprint/77532011-12-16T00:58:07ZOn Adjoint Dynamical SystemsTransformations of dynamical systems and organismic structures are discussed in terms of adjoint, simple adjoint and weak adjoint functors of organismic supercategories during development and evolution of organisms on markedly different timescales. A representation of nuclear transplants in terms of adjoint functors and a novel interpretation of nuclear transplant experiments is proposed. Three new theorems are proven for adjoint dynamical systems representing multi-potent developing cells and additional results are obtained for weak adjoint systems such as differentiated (specialized) cells.Prof. Dr. I. C. BaianuicbProf.Dr. Dragos Scripcariu2011-12-16T00:58:54Z2011-12-16T00:58:54Zhttp://cogprints.org/id/eprint/7743This item is in the repository with the URL: http://cogprints.org/id/eprint/77432011-12-16T00:58:54ZOrganismic Supercategories: III. Qualitative Dynamics of Systems The representation of biological systems by means of organismic supercategories, developed in previous papers, is further discussed. The different approaches to relational biology, developed by Rashevsky, Rosen and by Baianu and Marinescu, are compared with Qualitative Dynamics of Systems which was initiated by Henri Poincaré (1881). On the basis of this comparison some concrete results concerning dynamics of genetic system, development, fertilization, regeneration, analogies, and oncogenesis are derived.Prof. Dr. I.C. Baianuicb2004-06-19Z2011-03-11T08:55:37Zhttp://cogprints.org/id/eprint/3674This item is in the repository with the URL: http://cogprints.org/id/eprint/36742004-06-19ZOrganismic Supercategories and Qualitative Dynamics of Systems The representation of biological systems by means of organismic supercategories, developed in previous papers, is further discussed. The different approaches to relational biology, developed by Rashevsky, Rosen and by Baianu and Marinescu, are compared with Qualitative Dynamics of Systems which was initiated by Henri Poincaré (1881). On the basis of this comparison some concrete results concerning dynamics of genetic system, development, fertilization, regeneration, analogies, and oncogenesis are derived.Professor I.C. Baianuicb2011-12-16T00:58:58Z2011-12-16T00:58:58Zhttp://cogprints.org/id/eprint/7752This item is in the repository with the URL: http://cogprints.org/id/eprint/77522011-12-16T00:58:58ZOrganismic Supercategores: II. On Multistable Systems
The representation of biological systems in terms of organismic supercategories, introduced in previous papers by Baianu et al. (Bull. Math. Biophysics,30, 625–636;31, 59–70) is further discussed. To state more clearly this representation some new definitions are introduced. Also, some necessary changes in axiomatics are made. The conclusion is reached that any organismic supercategory has at least one superpushout, and this expresses the fact that biological systems are multistable. This way a connection between some results of Rashevsky’s theory of organismic sets and our results becomes obvious.Prof. Dr. I.C. Baianuicb2004-10-06Z2011-12-16T00:59:02Zhttp://cogprints.org/id/eprint/3831This item is in the repository with the URL: http://cogprints.org/id/eprint/38312004-10-06ZOrganismic Supercategories: I. Proposals for a General Unified Theory of Systems- Classical, Quantum, and Complex Biological Systems.
The representation of physical and complex biological systems in terms of organismic supercategories was introduced in 1968 by Baianu and Marinescu in the attached paper which was published in the Bulletin of Mathematical Biophysics, edited by Nicolas Rashevsky. The different approaches to relational biology, developed by Rashevsky, Rosen and by Baianu et al.(1968,1969,1973,1974,1987,2004)were later discussed.
The present paper is an attempt to outline an abstract unitary theory of systems. In the introduction some of the previous abstract representations of systems are discussed. Also a possible connection of abstract representations of systems with a general theory of measure is proposed. Then follow some necessary definitions and authors' proposals for an axiomatic theory of systems. Finally some concrete examples are analyzed in the light of the proposed theory.
An abstract representation of biological systems from the standpoint of the theory of supercategories is presented. The relevance of such representations forG-relational biologies is suggested. In section A the basic concepts of our representation, that is class, system, supercategory and measure are introduced. Section B is concerned with the mathematical representation starting with some axioms and principles which are natural extensions of the current abstract representations in biology. Likewise, some extensions of the principle of adequate design are introduced in section C. Two theorems which present the connection between categories and supercategories are proved. Two other theorems concerning the dynamical behavior of biological and biophysical systems are derived on the basis of the previous considerations. Section D is devoted to a general study of oscillatory behavior in enzymic systems, some general quantitative relations being derived from our representation. Finally, the relevance of these results for a quantum theoretic approach to biology is discussed.
http://www.springerlink.com/content/141l35843506596h/Prof. Dr. I.C. BaianuicbDr. Mircea M. Marinescu