Modèles probabilistes formels pour problèmes cognitifs usuels

Bibliographic Data

ID12110207
AuthorsPierre Bessière (0000-0002-8620-2505, Institut Systèmes Intelligents et de Robotique), Julien Diard (0000-0003-0673-477X, Centre National de la Recherche Scientifique), Francis Colas (0000-0002-7449-7676)
Year2016
Volume65
Issue1
Pages111-141
Publication date2016-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueIntellectica Revue de l Association pour la Recherche Cognitive (JOURNAL)
Journal identifiersISSN: 0769-4113 • E-ISSN: 2273-9262
PublisherPERSEE Program (PUBLISHER)
DOI10.3406/intel.2016.1792
OpenAlexW2505617723
LanguageCA
References cited6

Formal probabilistic models for common cognitive problems. 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

Bayesian probability · Cognition · Cognitive architecture · Cognitive science · Epistemology · Formalism (music · Modularity (biology · Probabilistic logic · Subjectivism · Computer Science · Neuroscience · Philosophy · Psychology · Semantic Web and Ontologies · Artificial Intelligence

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