Interac-DEC-MDP : Towards the use of interactions in DEC-MDP
Identifieur interne : 003D97 ( Crin/Curation ); précédent : 003D96; suivant : 003D98Interac-DEC-MDP : Towards the use of interactions in DEC-MDP
Auteurs : Vincent Thomas ; Christine Bourjot ; Vincent ChevrierSource :
English descriptors
Abstract
This article presents a new formalism Interac-DEC-MDP whose aim is to introduce the concept of interaction in Decentralized Markov Decision Process and which has been inspired by biology. The aim of this formalism, Interac-DEC-MDP, is to describe and represent interactions among agents. The outcome of interactions is decided collectively by two agents and is in charge of the distribution of local rewards. We have modeled a biological experiment within this formalism. A simple learning algorithm applied on this formalism generates a more efficient collective behavior than without interactions.
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<author><name sortKey="Thomas, Vincent" sort="Thomas, Vincent" uniqKey="Thomas V" first="Vincent" last="Thomas">Vincent Thomas</name>
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<author><name sortKey="Bourjot, Christine" sort="Bourjot, Christine" uniqKey="Bourjot C" first="Christine" last="Bourjot">Christine Bourjot</name>
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<author><name sortKey="Chevrier, Vincent" sort="Chevrier, Vincent" uniqKey="Chevrier V" first="Vincent" last="Chevrier">Vincent Chevrier</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>interaction</term>
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<front><div type="abstract" xml:lang="en" wicri:score="2672">This article presents a new formalism Interac-DEC-MDP whose aim is to introduce the concept of interaction in Decentralized Markov Decision Process and which has been inspired by biology. The aim of this formalism, Interac-DEC-MDP, is to describe and represent interactions among agents. The outcome of interactions is decided collectively by two agents and is in charge of the distribution of local rewards. We have modeled a biological experiment within this formalism. A simple learning algorithm applied on this formalism generates a more efficient collective behavior than without interactions.</div>
</front>
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<BibTex type="inproceedings"><ref>thomas04b</ref>
<crinnumber>A04-R-109</crinnumber>
<category>3</category>
<equipe>MAIA</equipe>
<author><e>Thomas, Vincent</e>
<e>Bourjot, Christine</e>
<e>Chevrier, Vincent</e>
</author>
<title>Interac-DEC-MDP : Towards the use of interactions in DEC-MDP</title>
<booktitle>{Third International Joint Conference on Autonomous Agents and Multi-Agent Systems - AAMAS'04, New York, USA}</booktitle>
<year>2004</year>
<pages>1450-1451</pages>
<month>Jul</month>
<keywords><e>markov decision process</e>
<e>multi-agent system</e>
<e>interaction</e>
<e>learning</e>
</keywords>
<abstract>This article presents a new formalism Interac-DEC-MDP whose aim is to introduce the concept of interaction in Decentralized Markov Decision Process and which has been inspired by biology. The aim of this formalism, Interac-DEC-MDP, is to describe and represent interactions among agents. The outcome of interactions is decided collectively by two agents and is in charge of the distribution of local rewards. We have modeled a biological experiment within this formalism. A simple learning algorithm applied on this formalism generates a more efficient collective behavior than without interactions.</abstract>
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