Market-based dynamic task allocation using heuristically accelerated reinforcement learning

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dc.contributor.authorGURZONI JUNIOR, J. A.
dc.contributor.authorFlavio Tonidandel
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-0345-668X
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2022-01-12T22:03:19Z
dc.date.available2022-01-12T22:03:19Z
dc.date.issued2011-10-10
dc.description.abstractThis paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time. The performance of the task allocation mechanism is evaluated and compared in different implementation variants, and results show that the proposed MRTA system significantly increases the team performance, when compared to pre-programmed team behavior algorithms. © 2011 Springer-Verlag.
dc.description.firstpage365
dc.description.lastpage376
dc.description.volume7026 LNAI
dc.identifier.citationGURZONI JUNIOR, J. A.; TONIDANDEL, F.Market-based dynamic task allocation using heuristically accelerated reinforcement learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 7026, p. 365-376, Oct. 2011.
dc.identifier.doi10.1007/978-3-642-24769-9_27
dc.identifier.issn0302-9743
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4196
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguageMulti-Robot Task Allocation
dc.subject.otherlanguageReinforcement Learning
dc.subject.otherlanguageRoboCup Robot Soccer
dc.titleMarket-based dynamic task allocation using heuristically accelerated reinforcement learning
dc.typeArtigo de evento
fei.scopus.citations9
fei.scopus.eid2-s2.0-80054815837
fei.scopus.subjectDynamic task allocation
fei.scopus.subjectMulti-robot task allocation
fei.scopus.subjectRoboCup robot
fei.scopus.subjectRoboCup Small Size League
fei.scopus.subjectTask allocation
fei.scopus.subjectTeam performance
fei.scopus.updated2024-12-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80054815837&origin=inward
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