Construction de systèmes multi-agents par apprentissage collectif à base d'interactions
Identifieur interne : 004E79 ( Main/Merge ); précédent : 004E78; suivant : 004E80Construction de systèmes multi-agents par apprentissage collectif à base d'interactions
Auteurs : Vincent Thomas [France] ; Christine Bourjot [France] ; Vincent Chevrier [France]Source :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2007.
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- topic : Intelligence artificielle.
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Abstract
This article deals with formal approaches to build multi-agent systems. The goal of the conducted works was to propose decentralized learning techniques to build the bejavior of social agents. This article presents an original formalism, the interac-DECPOMDP, in which agents can directly interact. On the basis of this formalism, this article proposes a decentralized learning algorithm based on a heuristic distribution of rewards during interactions. Experiments have validated its ability to automatically build collective behaviors. The presented techniques could then constitute a mean to operationalize self-organization in order to solve problems.
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Pascal:08-0301029Le document en format XML
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last="Thomas">Vincent Thomas</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>Équipe MAIA -tranche C Laboratoire LORIA Campus scientifique-P. 239</s1><s2>54506 Vandœuvre-les-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Bourjot, Christine" sort="Bourjot, Christine" uniqKey="Bourjot C" first="Christine" last="Bourjot">Christine Bourjot</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>Équipe MAIA -tranche C Laboratoire LORIA Campus scientifique-P. 239</s1><s2>54506 Vandœuvre-les-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation></author><author><name sortKey="Chevrier, Vincent" sort="Chevrier, Vincent" uniqKey="Chevrier V" first="Vincent" last="Chevrier">Vincent Chevrier</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>Équipe MAIA -tranche C Laboratoire LORIA Campus scientifique-P. 239</s1><s2>54506 Vandœuvre-les-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandœuvre-lès-Nancy</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Revue d'intelligence artificielle</title><title level="j" type="abbreviated">Rev. intell. artif.</title><idno type="ISSN">0992-499X</idno><imprint><date when="2007">2007</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Revue d'intelligence artificielle</title><title level="j" type="abbreviated">Rev. intell. artif.</title><idno type="ISSN">0992-499X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial intelligence</term><term>Collective learning</term><term>Collective process</term><term>Construction system</term><term>Formal method</term><term>Heuristic method</term><term>Learning algorithm</term><term>Learning systems</term><term>Markov model</term><term>Multiagent system</term><term>Reward</term><term>Self organization</term><term>Social interaction</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Système apprentissage</term><term>Apprentissage collectif</term><term>Méthode formelle</term><term>Système multiagent</term><term>Intelligence artificielle</term><term>Autoorganisation</term><term>Système construction</term><term>Récompense</term><term>Phénomène collectif</term><term>Interaction sociale</term><term>Algorithme apprentissage</term><term>Méthode heuristique</term><term>Modèle Markov</term><term>.</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Intelligence artificielle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This article deals with formal approaches to build multi-agent systems. The goal of the conducted works was to propose decentralized learning techniques to build the bejavior of social agents. This article presents an original formalism, the interac-DECPOMDP, in which agents can directly interact. On the basis of this formalism, this article proposes a decentralized learning algorithm based on a heuristic distribution of rewards during interactions. Experiments have validated its ability to automatically build collective behaviors. The presented techniques could then constitute a mean to operationalize self-organization in order to solve problems.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Vandœuvre-lès-Nancy</li></settlement></list><tree><country name="France"><region name="Grand Est"><name sortKey="Thomas, Vincent" sort="Thomas, Vincent" uniqKey="Thomas V" first="Vincent" last="Thomas">Vincent Thomas</name></region><name sortKey="Bourjot, Christine" sort="Bourjot, Christine" uniqKey="Bourjot C" first="Christine" last="Bourjot">Christine Bourjot</name><name sortKey="Chevrier, Vincent" sort="Chevrier, Vincent" uniqKey="Chevrier V" first="Vincent" last="Chevrier">Vincent Chevrier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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