Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools
Identifieur interne : 001903 ( Hal/Corpus ); précédent : 001902; suivant : 001904Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools
Auteurs : François Klein ; Christine Bourjot ; Vincent ChevrierSource :
English descriptors
Abstract
Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.
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DOI: 10.1007/978-3-642-02562-4_10
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Hal:inria-00400348Le document en format XML
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When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools</title><author role="aut"><persName><forename type="first">François</forename><surname>Klein</surname></persName><email>klein@loria.fr</email><idno type="halauthor">67273</idno><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Christine</forename><surname>Bourjot</surname></persName><email>christine.bourjot@loria.fr</email><idno type="halauthor">58996</idno><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Vincent</forename><surname>Chevrier</surname></persName><email>chevrier@loria.fr</email><idno type="halauthor">58990</idno><affiliation ref="#struct-2355"></affiliation></author><editor role="depositor"><persName><forename>François</forename><surname>Klein</surname></persName><email>klein@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2009-06-30 15:34:02</date><date type="whenModified">2016-05-18 08:56:36</date><date type="whenReleased">2009-06-30 15:50:31</date><date type="whenProduced">2009</date><date type="whenEndEmbargoed">2009-06-30</date><ref type="file" target="https://hal.inria.fr/inria-00400348/document"><date notBefore="2009-06-30"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00400348/file/klein.pdf"><date notBefore="2009-06-30"></date></ref></edition><respStmt><resp>contributor</resp><name key="103600"><persName><forename>François</forename><surname>Klein</surname></persName><email>klein@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00400348</idno><idno type="halUri">https://hal.inria.fr/inria-00400348</idno><idno type="halBibtex">klein:inria-00400348</idno><idno type="halRefHtml">Alexander Artikis and Gauthier Picard and Laurent Vercouter. Engineering Societies in the Agents World IX - 9th International Workshop, ESAW 2008, Saint-Etienne, France, September 24-26, 2008, Revised Selected Papers, 5485, Springer-Verlag Berlin Heidelberg, pp.173-190, 2009, Lecture Notes in Computer Science, 978-3-642-02561-7. <10.1007/978-3-642-02562-4_10></idno><idno type="halRef">Alexander Artikis and Gauthier Picard and Laurent Vercouter. Engineering Societies in the Agents World IX - 9th International Workshop, ESAW 2008, Saint-Etienne, France, September 24-26, 2008, Revised Selected Papers, 5485, Springer-Verlag Berlin Heidelberg, pp.173-190, 2009, Lecture Notes in Computer Science, 978-3-642-02561-7. <10.1007/978-3-642-02562-4_10></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="0">Not set</note><note type="popular" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools</title><author role="aut"><persName><forename type="first">François</forename><surname>Klein</surname></persName><email>klein@loria.fr</email><idno type="halAuthorId">67273</idno><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Christine</forename><surname>Bourjot</surname></persName><email>christine.bourjot@loria.fr</email><idno type="halAuthorId">58996</idno><affiliation ref="#struct-2355"></affiliation></author><author role="aut"><persName><forename type="first">Vincent</forename><surname>Chevrier</surname></persName><email>chevrier@loria.fr</email><idno type="halAuthorId">58990</idno><affiliation ref="#struct-2355"></affiliation></author></analytic><monogr><idno type="isbn">978-3-642-02561-7</idno><title level="m">Engineering Societies in the Agents World IX - 9th International Workshop, ESAW 2008, Saint-Etienne, France, September 24-26, 2008, Revised Selected Papers</title><editor>Alexander Artikis and Gauthier Picard and Laurent Vercouter</editor><imprint><publisher>Springer-Verlag Berlin Heidelberg</publisher><biblScope unit="serie">Lecture Notes in Computer Science</biblScope><biblScope unit="volume">5485</biblScope><biblScope unit="pp">173-190</biblScope><date type="datePub">2009</date></imprint></monogr><idno type="doi">10.1007/978-3-642-02562-4_10</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Control</term><term xml:lang="en">MAS</term><term xml:lang="en">experimental approach</term><term xml:lang="en">emergence</term><term xml:lang="en">global behaviour</term><term xml:lang="en">reinforcement learning</term></keywords><classCode scheme="halDomain" n="info.info-ma">Computer Science [cs]/Multiagent Systems [cs.MA]</classCode><classCode scheme="halTypology" n="COUV">Book section</classCode></textClass><abstract xml:lang="en">Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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