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dc.contributor.authorSaffidine, Abdallah
dc.contributor.authorCazenave, Tristan
dc.date.accessioned2010-11-19T08:22:34Z
dc.date.available2010-11-19T08:22:34Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5115
dc.language.isoenen
dc.relation.ispartofseries6515
dc.subjectMonte-Carlo tree searchen
dc.subjectMCTS solveren
dc.subject.ddc006.3en
dc.titleScore Bounded Monte-Carlo Tree Searchen
dc.typeCommunication / Conférence
dc.description.abstractenMonte-Carlo Tree Search (MCTS) is a successful algorithm used in many state of the art game engines. We propose to improve a MCTS solver when a game has more than two outcomes. It is for example the case in games that can end in draw positions. In this case it improves significantly a MCTS solver to take into account bounds on the possible scores of a node in order to select the nodes to explore. We apply our algorithm to solving Seki in the game of Go and to Connect Four.en
dc.identifier.citationpages93-104en
dc.relation.ispartofseriestitleLecture Notes in Computer Science
dc.relation.ispartofseriesnumber6515
dc.relation.ispartoftitleComputers and Games 7th International Conference, CG 2010, Kanazawa, Japan, September 24-26, 2010, Revised Selected Papers
dc.relation.ispartofeditorPlaat, Aske
dc.relation.ispartofeditorIida, Hiroyuki
dc.relation.ispartofeditorvan den Herik, H. Jaap
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin
dc.relation.ispartofdate2011
dc.relation.ispartofpages277
dc.relation.ispartofurlhttp://dx.doi.org/10.1007/978-3-642-17928-0
dc.relation.isversionofdoihttp://dx.doi.org/ 10.1007/978-3-642-17928-0_9
dc.description.sponsorshipprivateouien
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-642-17927-3
dc.relation.conftitleInternational Conference on Computers and Games 2010en
dc.relation.confdate2010-09
dc.relation.confcityKanazawaen
dc.relation.confcountryJaponen


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