Bayesian Programming: life science modeling and robotics applications

Abstract : How to use an incomplete and uncertain model of the environment to perceive, infer, decide and act efficiently? This is the challenge both living and artificial cognitive systems have to face. Logic is by nature unable to deal with this question. The subjectivist approach to probability is an alternative to logic specifically designed to face this challenge. In this paper we introduce Bayesian Programming, a methodology, a for- malism and an inference engine to build and compute probabilistic models. The principles are illustrated with two examples: modeling human perception of structure from motion and playing to train a video game avatar.
Type de document :
Communication dans un congrès
ISRR, 2007, Japan
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Contributeur : Pierre Bessière <>
Soumis le : vendredi 14 novembre 2008 - 12:30:22
Dernière modification le : vendredi 4 janvier 2019 - 01:23:32
Document(s) archivé(s) le : lundi 7 juin 2010 - 19:57:06


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  • HAL Id : hal-00338776, version 1



Pierre Bessière, Francis Colas. Bayesian Programming: life science modeling and robotics applications. ISRR, 2007, Japan. 〈hal-00338776〉



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