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Combining UCT and Nested Monte-Carlo Search for Single-Player General Game Playing

dc.contributor.authorMéhat, Jean
dc.contributor.authorCazenave, Tristan
HAL ID: 743184
dc.date.accessioned2010-11-19T08:21:42Z
dc.date.available2010-11-19T08:21:42Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5113
dc.language.isoenen
dc.subjectNested Monte-Carlo Searchen
dc.subjectSingle-player gamesen
dc.subjectGeneral Game Playingen
dc.subjectUCTen
dc.subject.ddc006.3en
dc.titleCombining UCT and Nested Monte-Carlo Search for Single-Player General Game Playingen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenMonte-Carlo tree search has recently been very successful for game playing particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare Nested Monte-Carlo Search (NMC), Upper Confidence bounds for Trees (UCT-T), UCT with transposition tables (UCT+T) and a simple combination of NMC and UCT+T (MAX) on single-player games of the past GGP competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version.en
dc.relation.isversionofjnlnameIEEE Transactions on Computational Intelligence and AI in Games
dc.relation.isversionofjnlvol2
dc.relation.isversionofjnlissue4
dc.relation.isversionofjnldate2010
dc.relation.isversionofjnlpages271-277
dc.relation.isversionofdoihttp://dx.doi.org/10.1109/TCIAIG.2010.2088123
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherIEEEen
dc.subject.ddclabelIntelligence artificielleen


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