MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
Identifieur interne : 003003 ( Hal/Curation ); précédent : 003002; suivant : 003004MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
Auteurs : Daniel Szer [France] ; François Charpillet [France] ; Shlomo Zilberstein [États-Unis]Source :
Abstract
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multi-robot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.
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<author><name sortKey="Zilberstein, Shlomo" sort="Zilberstein, Shlomo" uniqKey="Zilberstein S" first="Shlomo" last="Zilberstein">Shlomo Zilberstein</name>
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<country>États-Unis</country>
<placeName><settlement type="city">Amherst (Massachusetts)</settlement>
<region type="state">Massachusetts</region>
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<front><div type="abstract" xml:lang="en">We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multi-robot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs</title>
<author role="aut"><persName><forename type="first">Daniel</forename>
<surname>Szer</surname>
</persName>
<email>szer@loria.fr</email>
<idno type="halauthor">58656</idno>
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<author role="aut"><persName><forename type="first">François</forename>
<surname>Charpillet</surname>
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<email>francois.charpillet@loria.fr</email>
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<author role="aut"><persName><forename type="first">Shlomo</forename>
<surname>Zilberstein</surname>
</persName>
<email></email>
<idno type="halauthor">58658</idno>
<affiliation ref="#struct-91189"></affiliation>
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<editor role="depositor"><persName><forename>Daniel</forename>
<surname>Szer</surname>
</persName>
<email>szer@loria.fr</email>
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<editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2005-09-12 10:13:06</date>
<date type="whenModified">2016-05-18 08:55:24</date>
<date type="whenReleased">2005-09-12 10:19:29</date>
<date type="whenProduced">2005-07-26</date>
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<respStmt><resp>contributor</resp>
<name key="103092"><persName><forename>Daniel</forename>
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<email>szer@loria.fr</email>
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<idno type="halRefHtml">21st Conference on Uncertainty in Artificial Intelligence - UAI'2005, Jul 2005, Edinburgh/Scotland, 2005</idno>
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<idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
<idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno>
<idno type="stamp" n="INRIA2">INRIA 2</idno>
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<note type="popular" n="0">No</note>
<note type="peer" n="1">Yes</note>
<note type="proceedings" n="1">Yes</note>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs</title>
<author role="aut"><persName><forename type="first">Daniel</forename>
<surname>Szer</surname>
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<email>szer@loria.fr</email>
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<author role="aut"><persName><forename type="first">Shlomo</forename>
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<monogr><meeting><title>21st Conference on Uncertainty in Artificial Intelligence - UAI'2005</title>
<date type="start">2005-07-26</date>
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<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<abstract xml:lang="en">We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multi-robot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.</abstract>
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