Heuristique pour l'apprentissage automatique décentralisé d'interactions dans des systèmes multi-agents réactifs
Identifieur interne : 005375 ( Main/Merge ); précédent : 005374; suivant : 005376Heuristique pour l'apprentissage automatique décentralisé d'interactions dans des systèmes multi-agents réactifs
Auteurs : Vincent Thomas [France] ; Vincent Chevrier [France] ; Christine Bourjot [France]Source :
Descripteurs français
Abstract
Cet article propose une heuristique pour la construction automatique d'interactions dans un système multi-agent réactif. Il décrit le formalisme interac-DEC-POMDP qui permet de représenter dans un cadre homogène interactions et actions puis développe un algorithme fondé sur des échanges de récompenses et des techniques d'apprentissage par renforcement pour construire automatiquement et de manière entièrement décentralisée des organisations dans une sous-classe des interac-DEC-POMDPs. Ces techniques permettent en outre de produire à moindre coûts des comportements collectifs adaptatifs basés sur la notion d'interaction directe.
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Hal:inria-00104872Le document en format XML
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<profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>Système multi-agents réactifs</term>
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<front><div type="abstract" xml:lang="fr">Cet article propose une heuristique pour la construction automatique d'interactions dans un système multi-agent réactif. Il décrit le formalisme interac-DEC-POMDP qui permet de représenter dans un cadre homogène interactions et actions puis développe un algorithme fondé sur des échanges de récompenses et des techniques d'apprentissage par renforcement pour construire automatiquement et de manière entièrement décentralisée des organisations dans une sous-classe des interac-DEC-POMDPs. Ces techniques permettent en outre de produire à moindre coûts des comportements collectifs adaptatifs basés sur la notion d'interaction directe.</div>
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