--- abstract: "When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there are (at least) two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies (the `internal' approach); or (if this is feasible) one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down (or `foundationalist') approach where one tries to lay down general, a priori principles and a bottom-up (or `scientific') approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. It ends with a plea for the cooperation of the AI and Machine Learning communities as both learning and inference is needed if context is to make complete sense." altloc: - http://www.cpm.mmu.ac.uk/cpmrep52.html chapter: ~ commentary: ~ commref: ~ confdates: September 1999 conference: 'SEcond International and Interdisciplinay Conference on Modelling and Using Context - CONTEXT 1999' confloc: 'Trento, Italy' contact_email: ~ creators_id: [] creators_name: - family: Edmonds given: Bruce honourific: '' lineage: '' date: 1999 date_type: published datestamp: 2001-08-30 department: ~ dir: disk0/00/00/17/78 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: - family: Bouquet given: Paolo honourific: '' lineage: '' - family: Serafini given: Luciano honourific: '' lineage: '' - family: Brezillon given: Patrick honourific: '' lineage: '' - family: Benerecetti given: Massimo honourific: '' lineage: '' - family: Castellani given: Francesca honourific: '' lineage: '' eprint_status: archive eprintid: 1778 fileinfo: /style/images/fileicons/application_postscript.png;/1778/1/pragconA4.ps|/style/images/fileicons/application_pdf.png;/1778/5/praghol.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'context, knowledge transference, learning, inferrence, model, modelling, modeling, conditions of application, recognition, simulation, agent, Bak, network, GP, genetic programming, artificial stock market' lastmod: 2011-03-11 08:54:47 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 119-132 pubdom: FALSE publication: ~ publisher: Springer-Verlag refereed: TRUE referencetext: |- [1] Akman, V. (1997). Context as a Social Construct. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge. [2] Akman, V. and Surav, M. (1996). Steps Towards Formalizing Context. AI Magazine, 17:55-72. [3] Barwise, J. and Perry, J. (1983). Situations and Attitudes. Cambridge: MIT Press. [4] Chialvo, D. R. and Bak, P. (1997). Learning by Mistakes. Sante Fe Working Paper 97-08-077. [5] Drescher, G. L. (1991). Made-up Minds - A Constructivist Approach to Artificial Intelligence. Cambridge, MA: MIT Press. [6] Edmonds, B. (1998). Modelling Socially Intelligent Agents. Applied Artificial Intelligence, 12. [7] Edmonds, B. (forthcoming). Modelling Bounded Rationality In Agent-Based Simulations using the Evolution of Mental Models.In Brenner, T. (ed.), Computational Techniques for Modelling Learning in Economics, Kluwer. [8] Edmonds, B. (forthcoming). Capturing Social Embeddedness: a Constructivist Approach. Adaptive Behaviour. [9] Edmonds, B. A Simple-Minded Network Model with Context-like Objects. European Conference on Cognitive Science (ECCS'97), Manchester, April 1997. (http://www.cpm.mmu.ac.uk/cpmrep15.html) [10] Hayes, P. (1995). Contexts in Context. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge. [11] Hirst, G. (1997). Context as a Spurious Concept. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge. [12] McCarthy, J. (1996). A logical AI approach to context. Unpublished note, 6 February 1996. http://www-formal.stanford.edu/jmc/logical.html [13] Moss, S., Gaylard, H., Wallis, S. and Edmonds, B. (1998). SDML: A Multi-Agent Language for Organizational Modelling. Computational and Mathematical Organization Theory, 4, 43-69. [14] Palmer, R.G. et. al. (1994). Artificial Economic Life - A Simple Model of a Stockmarket. Physica D, 75:264-274. [15] Pearl, J. (forthcoming). An Axiomatic Characteriztion of Causal Counterfactuals. Foundations of Science. [16] Trun S. and Mitchell, T. M. (1995). Learning One More Thing. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI'95). San Mateo, CA: Morgan Kaufmann, 1217-1223. [17] Vaario, J. (1994). Artificial Life as Constructivist AI. Japanese Society of Instrument and Control Engineers, 33:65-71. [18] Wagner, A. (1997). Causality in Complex Systems. Sante Fe Working Paper 97-08-075. [19] Wheeler, M. and Clark, A. (forthcoming). Genic Representation: Reconciling Content and Causal Complexity. British journal for the Philosophy of Science. [20] Widmer, G. (1997). Tracking Context Changes through Meta-Learning. Machine Learning, 27:259-286. [21] Zadrozny, W. (1997). A Pragmatic Approach to Context. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge. relation_type: [] relation_uri: [] reportno: ~ rev_number: 14 series: ~ source: ~ status_changed: 2007-09-12 16:40:18 subjects: - cog-psy - comp-sci-art-intel - comp-sci-mach-learn - ling-prag - phil-sci succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: The Pragmatic Roots of Context type: confpaper userid: 192 volume: 1688