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Modèles probabilistes formels pour problèmes cognitifs usuels

Pierre Bessière 1, 2 Julien Diard 3 Francis Colas 4 
ISIR - Institut des Systèmes Intelligents et de Robotique
4 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.
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Submitted on : Friday, July 15, 2016 - 11:10:15 AM
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Pierre Bessière, Julien Diard, Francis Colas. Modèles probabilistes formels pour problèmes cognitifs usuels. Intellectica - La revue de l’Association pour la Recherche sur les sciences de la Cognition (ARCo), Association pour la Recherche sur la Cognition, 2016, Nouvelles approches en robotique cognitive, 65 (1), pp.111-141. ⟨10.3406/intel.2016.1792⟩. ⟨hal-01345697⟩



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