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dc.contributor.authorCazenave, Tristan
dc.date.accessioned2010-05-11T10:45:34Z
dc.date.available2010-05-11T10:45:34Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4151
dc.descriptionProceedings en ligne, 456-461en
dc.language.isoenen
dc.subjectRandom Gamesen
dc.subject.ddc006en
dc.titleNested Monte-Carlo Searchen
dc.typeCommunication / Conférence
dc.description.abstractenMany problems have a huge state space and no good heuristic to order moves so as to guide the search toward the best positions. Random games can be used to score positions and evaluate their interest. Random games can also be improved using random games to choose a move to try at each step of a game. Nested Monte-Carlo Search addresses the problem of guiding the search toward better states when there is no available heuristic. It uses nested levels of random games in order to guide the search. The algorithm is studied theoretically on simple abstract problems and applied successfully to three different games: Morpion Solitaire, SameGame and 16x16 Sudoku.en
dc.identifier.citationpages6en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelMéthodes informatiques spécialesen
dc.relation.conftitleIJCAI 2009 International Joint Conferences on Artificial Intelligenceen
dc.relation.confdate2009-07
dc.relation.confcityPasadenaen
dc.relation.confcountryÉtats-Unisen


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