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Nested Monte-Carlo Expression Discovery

dc.contributor.authorCazenave, Tristan
dc.date.accessioned2011-09-16T13:31:31Z
dc.date.available2011-09-16T13:31:31Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6971
dc.language.isoenen
dc.subjectalgorithmen
dc.subjectsingle player gamesen
dc.subjectGenetic Programmingen
dc.subjectNested Monte-Carloen
dc.subject.ddc005en
dc.titleNested Monte-Carlo Expression Discoveryen
dc.typeCommunication / Conférence
dc.description.abstractenNested Monte-Carlo search is a general algorithm that gives good results in single player games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given evaluation function. In this paper Nested Monte-Carlo Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Single player Nested Monte-Carlo Search is transformed in order to search expression trees rather than lists of moves. The resulting program achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.en
dc.relation.isversionofjnlnameFrontiers in Artificial Intelligence and Applications
dc.relation.isversionofjnlvol215
dc.relation.isversionofjnldate2010
dc.relation.isversionofjnlpages1057-1058
dc.relation.isversionofdoihttp://dx.doi.org/10.3233/978-1-60750-606-5-1057
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherIOS Press
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.conftitleECAI 2010 - 19th European Conference on Artificial Intelligenceen
dc.relation.confdate2010-08
dc.relation.confcityLisbonneen
dc.relation.confcountryPortugalen


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