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hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorCazenave, Tristan
HAL ID: 743184
*
dc.date.accessioned2013-10-01T14:45:48Z
dc.date.available2013-10-01T14:45:48Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11741
dc.language.isoenen
dc.subjectNested Monte-Carlo Searchen
dc.subjectPuzzleen
dc.subjectBeam Searchen
dc.subject.ddc006.3en
dc.titleMonte Carlo Beam Searchen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenMonte Carlo tree search is the state of the art for multiple games and for solving puzzles such as Morpion Solitaire. Nested Monte Carlo (NMC) search is a Monte Carlo tree search algorithm that works well for solving puzzles. We propose to enhance NMC search with beam search. We test the algorithm on Morpion Solitaire. Thanks to beam search, our program has been able to match the record score of 82 moves. Monte Carlo beam search achieves better scores in less time than NMC search alone.en
dc.relation.isversionofjnlnameIEEE Transactions on Computational Intelligence and AI in Games
dc.relation.isversionofjnlvol4en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages68-72en
dc.relation.isversionofdoi10.1109/TCIAIG.2011.2180723en
dc.relation.isversionofjnlpublisherIEEEen
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
hal.identifierhal-01498618*
hal.version1*
hal.update.actionupdateMetadata*
hal.author.functionaut


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