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Bayesian robot Programming

Lebeltel, Olivier and Bessiere, Pierre and Diard, Julien and Mazer, Emmanuel (2000) Bayesian robot Programming. [Departmental Technical Report]

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Abstract

We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics.

Item Type:Departmental Technical Report
Keywords:Robotics Bayes Pereception Inference Action
Subjects:Computer Science > Artificial Intelligence
Computer Science > Robotics
Computer Science > Statistical Models
ID Code:1670
Deposited By: Bessiere, Pierre
Deposited On:05 Jul 2001
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Alami et al., 1998

Alami, R., Chatila, R., Fleury, S., Ghallab, M. & Ingrand, F. ; (1998) ; An Architecture for Autonomy ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 315-337

Aycard, 1998

Aycard, O.; (1998) ; Architecture de contrôle pour robot mobile en environnement intérieur structuré ; Ph.D. thesis, Univeristé Henri Poincaré, Nancy, France

Bernhardt & Albright, 1993

Bernhardt, R. & Albright, S.L. (editors) ; (1993) ; Robot Calibration ; Chapman & Hall

Bessière et al., 1998a

Bessière, P., Dedieu, E., Lebeltel, O., Mazer, E. & Mekhnacha, K. ; (1998a) ; Interprétation ou Description (I) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs ; Intellectica ; Vol. 26-27, pp. 257-311; Paris, France

Bessière et al., 1998b

Bessière, P., Dedieu, E., Lebeltel, O., Mazer, E. & Mekhnacha, K. ; (1998a) ; Interprétation ou Description (I) : Fondements mathématiques de l’approche F+D ; Intellectica ; Vol. 26-27, pp. 313-336 ; Paris, France

Borrelly et al., 1998

Borrelly, J-J., Coste, E., Espiau, B., Kapellos, K., Pissard-Gibollet, R., Simon, D. & Turro, N.; (1998) ; The ORCCAD Architecture ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 338-359

Brafman et al., 1997

Brafman, R.I., Latombe, J-C., Moses, Y. & Shoham, Y.; (1997) ; Applications of a logic of knowledge to motion planning under uncertainty ; Journal of the ACM, vol.44(5), pp. 633-68

Bretthorst, 1988

Bretthorst, G.L. ; (1988) ; Bayesian spectrum analysis and parameter estimation ; Spinger Verlag

Brooks, 1986

Brooks, R.A. ; (1986) ; A robust layered control systems for a mobile robot ; IEEE Journal of Robotics and Automation ; Vol. 2(1), pp. 14-23

Cooper, 1990

Cooper, G. ; (1990) ; The computational complexity of probabilistic inference using Bayesian belief networks ; Artificial Intelligence, Vol. 42, pp. 393-405

Cox, 1961

Cox, R.T. ; (1961) ; The algebra of probable inference ; The John Hopkins Press, Baltimore, USA

Cox, 1979

Cox, R.T. ; (1979) ; Of inference and inquiry, an essay in inductive logic ; in The maximum entropy formalism, edited by Raphael D. Levine & Myron Tribus ; M.I.T. Press, U.S.A.

Dagum & Luby, 1993

Dagum, P. & Luby, M. ; (1993) ; Approximate probabilistic reasoning in Bayesian belief network is NP-Hard ; Artificial Intelligence, Vol. 60, pp. 141-153

Darwiche & Provan, 1997

Darwiche, A. and Provan, G. ; (1997) ; Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference ; Journal of Artificial Intelligence Research (JAIR), Vol. 6, pp. 147-176

Dedieu, 1995

Dedieu, E. ; (1995) ; La représentation contingente : Vers une reconciliation des approches fonctionnelles et structurelles de la robotique autonome. Thèse de troisième cycle INPG (Institut National Polytechnique de Grenoble) ; Grenoble, France

Dekhil & Henderson, 1998

Dekhil, M. & Henderson, T.C. ; (1998) ; Instrumented Sensor System Architecture ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 402-417

Delcher et al., 1996

Delcher, A.L., Grove, A.J., Kasif, S. and Pearl, J. ; (1996) ; Logarithmic-Time Updates and Queries in Probabilistic Networks ; Journal of Artificial Intelligence Research (JAIR) ; Vol. 4, pp. 37-59

Diard & Lebeltel, 1999

Diard, J. & Lebeltel, O. ; (1999) ; Bayesian Learning Experiments with a Khepera Robot in Experiments with the Mini-Robot Khepera : Proceedings of the 1st International Khepera Workshop, December 1999, Löffler Mondada Rückert (Editors), Paderborn, HNI-Verlagsschriftenreihe ; Band 64 ; Germany ; pp. 129-138 ;

Donald, 1988

Donald, B.R.; (1988) ; A geometric approach to error detection and recovery for robot motion planning with uncertainty ; Artificial Intelligence, vol.37, pp. 223-271

Erickson & Smith, 1988a

Erickson, G.J. & Smith, C.R. ; (1988a) ; Maximum-Entropy and Bayesian methods in science and engineering ; Volume 1 : Foundations ; Kluwer Academic Publishers

Erickson & Smith, 1988b

Erickson, G.J. & Smith, C.R. ; (1988b) ; Maximum-Entropy and Bayesian methods in science and engineering ; Volume 2 : Applications ; Kluwer Academic Publishers

Frey, 1998

Frey, B.J. ; (1998) ; Graphical Models for Machine Learning and Digital Communication ; MIT Press

Halpern, 1999a

Halpern, J.Y. ; (1999a) ; A Counterexample to Theorems of Cox and Fine ; Journal of Artificial Intelligence Research (JAIR), Vol. 10, pp. 67-85.

Halpern, 1999b

Halpern, J.Y. ; (1999b) ; Cox's Theorem Revisited ; Journal of Artificial Intelligence Research (JAIR), Vol. 11, pp. 429-435.

Jaakola & Jordan, 1999

Jaakkola, T.S. and Jordan, M.I. ; (1999) ; Variational Probabilistic Inference and the QMR-DT Network ; Journal of Artificial Intelligence Research (JAIR), Vol. 10, pp. 291-322

Jaynes, 1979

Jaynes, E.T. ; (1979) ; Where do we Stand on Maximum Entropy? ; in The maximum entropy formalism ; edited by Raphael D. Levine & Myron Tribus ; M.I.T. Press

Jaynes, 1982

Jaynes, E.T. ; (1982) ; On the rationale of maximum-entropy methods ; Proceedings of the IEEE

Jaynes, 1998

Jaynes, E.T. ; (1998) ; Probability theory - The logic of science ; unfinished book available at http://bayes.wustl.edu/etj/prob.html

Jordan & Jacobs, 1994

Jordan MI and Jacobs RA (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation ; Vol. 6, pp. 181-214.

Jordan, 1998

Jordan, M. ; (1998) ; Learning in Graphical Models ; MIT Press

Jordan et al., 1999

Jordan, M., Ghahramani, Z., Jaakkola, T.S. & Saul, L.K. ; (1999) ; An introduction to variational methods for graphical models ; In press, Machine Learning

Kaelbling, Littman & Cassandra, 1996

Kaelbling, L.P., Littman, M.L. & Cassandra, A.R.; (1996) ; Partially observable Markov decision processes for artificial intelligence ; Reasoning with Uncertainty in Robotics. International Workshop, RUR'95, Proceedings pp.146-62 ; Springer-Verlag

Kapur & Kesavan, 1992

Kapur, J.N., & Kesavan, H.K. ; (1992) ; Entropy optimization principles with applications ; Academic Press

Laplace, 1774

Laplace, Pierre Simon de (1774); Mémoire sur la probabilités des causes par les évènements; Mémoire de l’académie royale des sciences; Reprinted in Oeuvres complètes de Laplace, (vol. 8), Gauthier Villars, Paris, France

Laplace, 1814

Laplace, Pierre Simon de (1814); Essai philosphique sur les probabilités; Courcier Imprimeur, Paris; Reprinted in Oeuvres complètes de Laplace, (vol. 7), Gauthier Villars, Paris, France

Lauritzen & Spiegehalter, 1988

Lauritzen, S. & Spiegelhalter, D. ; (1988) ; Local computations with probabilities on graphical structures and their application to expert systems ; Journal of the Royal Stastical Society B ; Vol. 50, pp. 157-224

Lauritzen, 1996

Lauritzen, S. L. ; (1996) ; Graphical Models ; Oxford University Press

Lebetel, 1999

Lebeltel, O. ; (1999) ; Programmation Bayésienne des Robots ; Ph.D. Thesis, Institut National Polytechnique de Grenoble (INPG); Grenoble, France

Lozano-Perez et al., 1984

Lozano-Perez, T., Mason, M.T., Taylor, R.H.; (1984) ; Automatic synthesis of fine-motion strategies for robots ; International Journal of Robotics Research, vol.3(1), pp. 3-24

Maes, 1989

Maes, P. ; (1989) ; How to Do the Right Thing ; Connection Science Journal ; Vol. 1, N°3, pp. 291-323

Matalon, 1967

Matalon, B. ; (1967) ; Epistémologie des probabilités ; in Logique et connaissance scientifique edited by Jean Piaget ; Encyclopédie de la Pléiade ; Editions Gallimard ; Paris, France

Mazer et al., 1998

Mazer, E., Boismain, G., Bonnet des Tuves, J., Douillard, Y., Geoffroy, S., Dubourdieu, J., Tounsi, M. & Verdot, F.; (1998) ; START: an Industrial System for Teleoperation, Proc. of the IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1154-1159, Leuven (BE)

Mekhnacha, 1999

Mekhnacha, K. ; (1999) ; Méthodes probabilistes baysiennes pour la prise en compte des incertitudes géométriques : Application à la CAO-robotique ; Ph.D. thesis INPG (Institut National Polytechnique de Grenoble), Grenoble, France

Mohammad-Djafari & Demoment, 1992

Mohammad-Djafari, A.& Demoment, G. ; (1992) ; Maximum entropy and bayesian methods ; Kluwer Academic Publishers

Pearl, 1988

Pearl, J. ; (1988) ; Probabilistic reasoning in intelligent systems : Networks of plausible inference ; Morgan Kaufmann Publishers ; San Mateo, California, USA

Robert, 1990

Robert, C. ; (1990) ; An entropy concentration theorem: applications ; in artificial intelligence and descriptive statistics ; Journal of Applied Probabilities

Robinson, 1965

Robinson, J.A. ; (1965) ; A Machine Oriented Logic Based on the Resolution Principle ; Jour. Assoc. Comput. Mach.; vol. 12

Robinson, 1979

Robinson, J.A. ; (1979) ; Logic : Form and Function ; North-Holland, New York, USA

Robinson & Sibert, 1983a

Robinson, J.A. & Sibert, E.E. ; (1983a) ; LOGLISP : an alternative to PROLOG ; Machine Intelligence, Vol. 10.

Robinson & Sibert, 1983b

Robinson, J.A. & Sibert, E.E. ; (1983b) ; LOGLISP : Motivation, design and implementation ; Machine Intelligence, Vol. 10.

Ruiz et al., 1998

Ruiz, A., Lopez-de-Teruel, P.E. and Garrido, M.C. ; (1998) ; Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians ; Journal of Artificial Intelligence Research (JAIR) ; Vol. 9, pp. 167-217

Saul et al., 1996

Saul, L.K., Jaakkola, T. and Jordan, M.I. ; (1996) ; Mean Field Theory for Sigmoid Belief Networks ; Journal of Artificial Intelligence Research (JAIR), Vol. 4, pp. 61-76

Schneider et al., 1998

Schneider, S.A., Chen, V.W., Pardo-Castellote, G., Wang, H.H.; (1998) ; ControlShell: A Software Architecture for Complex Electromechanical Systems ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 360-380

Smith & Grandy, 1985

Smith, C.R. & Grandy, W.T. Jr. ; (1985) ; Maximum-Entropy and bayesian methods in inverse problems ; D. Reidel Publishing Company

Tarentola, 1987

Tarentola, A. ; (1987) ; Inverse Problem Theory: Methods for data fitting and model parameters estimation ; Elsevier ; New York, USA

Thrun, 1998

Thrun, S.; (1998) ; Bayesian landmark learning for mobile robot localization ; Machine Learning, vol. 33(1), pp.41-76

Zhang & Poole, 1996

Zhang, N.L. and Poole, D. ; (1996) ; Exploiting Causal Independence in Bayesian Network Inference ; Journal of Artificial Intelligence Research (JAIR), Vol. 5, pp. 301-328

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