inférence approximée comme un problème d'optimisation ; en marginalisant, nous cherchons finalement les « pics » de probabilité dans un espace pouvant être très grand. Différentes techniques existent pour estimer la marginalisation et trouver les solutions les plus probables ; citons les algorithmes génétiques, les algorithmes de type « recuit simulé ,
En fait, ces méthodes de résolution sont spécifiques et indissociables d'un ensemble d'hypothèses et de choix de modélisation du problème traité. Ainsi, nous sommes conduit à reconnaître, qu'à terme, 1998. ,
The generalized distributive law, IEEE Transactions on Information Theory, vol.46, issue.2, pp.325-343, 2000. ,
DOI : 10.1109/18.825794
An Architecture for Autonomy, The International Journal of Robotics Research, vol.17, issue.4, pp.315-337, 1998. ,
DOI : 10.1177/027836499801700402
URL : https://hal.archives-ouvertes.fr/hal-00123273
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002. ,
DOI : 10.1109/78.978374
Architecture de contrôle pour robot mobile en environnement intérieur structuré, Thèse de doctorat, 1998. ,
« Interprétation ou description (I) : proposition pour une théorie probabiliste des systèmes cognitifs sensorimoteurs, pp.26-27, 1998. ,
« Interprétation ou description (II) : fondements mathématiques de l'approche F+D, pp.26-27, 1998. ,
Survey : Probabilistic methodology and techiques for artefact conception and development, rapport de recherche, p.4730, 2003. ,
The ORCCAD Architecture, The ORCCAD architecture, pp.338-359, 1998. ,
DOI : 10.1177/027836499801700403
URL : https://hal.archives-ouvertes.fr/hal-00930119
Applications of a logic of knowledge to motion planning under uncertainty, Journal of the ACM, vol.44, issue.5, pp.633-668, 1997. ,
DOI : 10.1145/265910.265912
« Bayesian spectrum analysis and parameter estimation », Lecture Notes in Statictics, 1988. ,
A robust layered control system for a mobile robot, IEEE Journal on Robotics and Automation, vol.2, issue.1, pp.14-23, 1986. ,
DOI : 10.1109/JRA.1986.1087032
The computational complexity of probabilistic inference using bayesian belief networks, Artificial Intelligence, vol.42, issue.2-3, pp.393-405, 1990. ,
DOI : 10.1016/0004-3702(90)90060-D
The Algebra of Probable Inference, American Journal of Physics, vol.31, issue.1, 1961. ,
DOI : 10.1119/1.1969248
Approximating probabilistic inference in Bayesian belief networks is NP-hard, Artificial Intelligence, vol.60, issue.1, pp.141-153, 1993. ,
DOI : 10.1016/0004-3702(93)90036-B
« Query DAGs : a practical paradigm for implementing belief? networks inference, Journal of Artificial Intelligence Research, vol.6, pp.147-176, 1997. ,
Instrumented Sensor System Architecture, The International Journal of Robotics Research, vol.17, issue.4, pp.402-417, 1998. ,
DOI : 10.1177/027836499801700406
« Bayesian learning experiments with a Khepera robot, Experiments with the Mini-Robot Khepera Proceedings of the 1st International Khepera Workshop, pp.129-138, 1999. ,
« Bayesian programming and hierarchical learning in robotics, SAB2000 Proceedings Supplement Book, Publication of the International Society for Adaptive Behavior, 2000. ,
A geometric approach to error detection and recovery for robot motion planning with uncertainty, Artificial Intelligence, vol.37, issue.1-3, pp.223-271, 1988. ,
DOI : 10.1016/0004-3702(88)90056-2
Maximum-Entropy and Bayesian methods in science and engineering, Foundations, vol.1, 1988. ,
Maximum-Entropy and Bayesian methods in science and engineering, 1988. ,
Graphical Models for Machine Learning and Digital Communication, 1998. ,
« A probabilistic approach to collaborative multi-robot localization, Autonomous Robots, vol.8, issue.3, pp.325-344, 2000. ,
DOI : 10.1023/A:1008937911390
« Experimental comparison of localization methods », proc, RSJ International Conference on Intelligent Robots and Systems, 1998. ,
DOI : 10.1109/iros.1998.727280
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.6293
« A counterexample to theorems of Cox and Fine, Journal of Artificial Intelligence Research, vol.10, pp.67-85, 1999. ,
« Variational probabilistic inference and the QMR-DT network, Journal of Artificial Intelligence Research, vol.10, pp.291-322, 1999. ,
« Where do we stand on maximum entropy? », The maximum entropy formalism, 1979. ,
On the rationale of maximum-entropy methods, Proceedings of the IEEE, vol.70, issue.9, pp.939-952, 1982. ,
DOI : 10.1109/PROC.1982.12425
Probability theory -The logic of science, 2003. ,
Hierarchical Mixtures of Experts and the EM Algorithm, Neural Computation, vol.26, issue.2, pp.181-214, 1994. ,
DOI : 10.1214/aos/1176346060
Learning in Graphical Models, 1998. ,
DOI : 10.1007/978-94-011-5014-9
An Introduction to Variational Methods for Graphical Models, Machine Learning, pp.183-233, 1999. ,
DOI : 10.1007/978-94-011-5014-9_5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.3844
« Partially observable Markov decision processes for artificial intelligence, Proceedings of International Workshop Reasoning with Uncertainty in Robotics, pp.146-62, 1996. ,
DOI : 10.1007/3-540-60343-3_22
« Acting under uncertainty : discrete bayesian models for mobile-robot navigation, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 1996. ,
Entropy Optimization Principles and Their Applications, 1992. ,
DOI : 10.1007/978-94-011-2430-0_1
« Improved occupancy grids for map building, Autonomous Robots, vol.4, issue.4, pp.351-367, 1997. ,
DOI : 10.1023/A:1008806422571
« Markov localization using correlation, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp.1154-1159, 1999. ,
« Toward hierachical decomposition for planning in uncertain environments, Workshop on planning under Uncertainty and Incomplete Information at the 2001 International Joint Conference on Artificial Intelligence, 2001. ,
Mémoire sur la probabilités des causes par les évènements », Mémoire de l'académie royale des sciences, p.1774 ,
« Local computations with probabilities on graphical structures and their application to expert systems, Journal of the Royal Stastical Society, vol.50, issue.19, pp.157-224, 1988. ,
Programmation bay??sienne des robots, Thèse de doctorat, 1999. ,
DOI : 10.3166/ria.18.261-298
URL : https://hal.inria.fr/inria-00182069/file/BRP_RIA.pdf
Automatic Synthesis of Fine-Motion Strategies for Robots, The International Journal of Robotics Research, vol.24, issue.9, pp.3-24, 1984. ,
DOI : 10.1177/027836498400300101
« Introduction to Monte-Carlo methods », Learning in Graphical Models, pp.175-204, 1996. ,
How to do the Right Thing, How to Do the Right Thing, pp.291-323, 1989. ,
DOI : 10.2307/1884852
« Epistémologie des probabilités, Logique et connaissance scientifique, pp.927-991, 1967. ,
« START: an industrial system for teleoperation, Proc. of the IEEE Int. Conf. on Robotics and Automation, pp.1154-1159, 1998. ,
The design and implementation of a Bayesian CAD modeler for robotic applications, Advanced Robotics, vol.9, issue.4, 2001. ,
DOI : 10.1163/156855301750095578
URL : https://hal.archives-ouvertes.fr/hal-00019182
Maximum entropy and bayesian methods, 1992. ,
DOI : 10.1007/978-94-017-2217-9
URL : https://hal.archives-ouvertes.fr/hal-00599193
Probabilistic inference using Markov chain Monte-Carlo Methods, 1993. ,
« An entropy concentration theorem: applications », in artificial intelligence and descriptive statistics, Journal of Applied Probabilities, 1990. ,
Optimal selection of uncertain actions by maximizing expected utility, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375), pp.17-25, 2000. ,
DOI : 10.1109/CIRA.1999.809953
« Probabilistic inference from arbitrary uncertainty using mixtures of factorized generalized gaussians », Journal of Artificial Intelligence Research, vol.9, pp.167-217, 1998. ,
ControlShell: A Software Architecture for Complex Electromechanical Systems, The International Journal of Robotics Research, vol.17, issue.4, pp.360-380, 1998. ,
DOI : 10.1177/027836499801700404
Maximum-Entropy and bayesian methods in inverse problems, 1985. ,
DOI : 10.1007/978-94-017-2221-6
Inverse problem theory: methods for data fitting and model parameters estimation, 1987. ,
« Bayesian landmark learning for mobile robot localization, Machine Learning, pp.41-76, 1998. ,
« A probabilistic approach to concurrent mapping and localization for mobile robots, Autonomous Robots, vol.5, issue.3/4, pp.253-271, 1998. ,
DOI : 10.1023/A:1008806205438
Probabilistic algorithms in robotic, 2000. ,
« Exploiting causal independence in bayesian network inference, Journal of Artificial Intelligence Research, vol.5, pp.301-328, 1996. ,