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Monte Carlo Graph Coloring

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorCazenave, Tristan
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorNegrevergne, Benjamin
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorSikora, Florian
HAL ID: 742949
ORCID: 0000-0003-2670-6258
dc.date.accessioned2021-10-22T07:52:45Z
dc.date.available2021-10-22T07:52:45Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22075
dc.language.isoenen
dc.subjectMonte Carloen
dc.subject.ddc006.3en
dc.titleMonte Carlo Graph Coloringen
dc.typeCommunication / Conférence
dc.description.abstractenGraph Coloring is probably one of the most studied and famous problem in graph algorithms. Exact methods fail to solve instances with more than few hundred vertices, therefore, a large number of heuristics have been proposed. Nested Monte Carlo Search (NMCS) and Nested Rollout Policy Adaptation (NRPA) are Monte Carlo search algorithms for single player games. Surprisingly, few work has been dedicated to evaluating Monte Carlo search algorithms to combinatorial graph problems. In this paper we expose how to efficiently apply Monte Carlo search to Graph Coloring and compare this approach to existing ones.en
dc.subject.ddclabelIntelligence artificielleen
dc.relation.conftitleIJCAI Workshopen
dc.relation.confdate2021-01
dc.relation.confcityYokohama (virtual)en
dc.relation.confcountryJapanen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2021-10-22T07:51:01Z
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hal.author.functionaut
hal.author.functionaut


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