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A Monte Carlo Tree Search Based Conflict-Driven Clause Learning SAT Solver

dc.contributor.authorSchloeter, Jens
dc.contributor.editorEibl, Maximilian
dc.contributor.editorGaedke, Martin
dc.date.accessioned2017-08-28T23:48:11Z
dc.date.available2017-08-28T23:48:11Z
dc.date.issued2017
dc.description.abstractMost modern state-of-the-art Boolean Satisfiability (SAT) solvers are based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm and exploit techniques like unit propagation and Conflict-Driven Clause Learning. Even though this approach proved to be successful in practice and most recent publications focus on improving it, the success of the Monte Carlo Tree Search (MCTS) algorithm in other domains led to research in using it to solve SAT problems. While a MCTS-based algorithm was successfully used to solve SAT problems, a number of established SAT solving techniques like clause learning and parallelization were not included in the algorithm. Therefore this paper presents ways to combine the MCTS-based SAT solving approach with established SAT solving techniques like Conflict-Driven Clause Learning and shows that the addition of those techniques improves the performance of a plain MCTS-based SAT solving algorithm.en
dc.identifier.doi10.18420/in2017_257
dc.identifier.isbn978-3-88579-669-5
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-275
dc.subjectBoolean Satisfiability
dc.subjectSAT Solving
dc.subjectMonte Carlo Tree Search
dc.subjectConflict-Driven Clause Learning
dc.titleA Monte Carlo Tree Search Based Conflict-Driven Clause Learning SAT Solveren
gi.citation.endPage2560
gi.citation.startPage2549
gi.conference.date25.-29. September 2017
gi.conference.locationChemnitz
gi.conference.sessiontitleStudierendenkonferenz Informatik 2017 (SKILL 2017)

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