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dc.contributor.convenorFrancesca Rossien_US
dc.contributor.authorCai, Shaowei
dc.contributor.authorSu, Kaile
dc.contributor.editorFrancesca Rossi
dc.date.accessioned2017-12-05T01:12:08Z
dc.date.available2017-12-05T01:12:08Z
dc.date.issued2013
dc.date.modified2014-02-12T22:28:59Z
dc.identifier.refurihttp://ijcai13.org/en_US
dc.identifier.urihttp://hdl.handle.net/10072/56724
dc.description.abstractIt is widely acknowledged that stochastic local search (SLS) algorithms can efficiently find models of satisfiable formulae for the Boolean Satisfiability (SAT) problem. There has been much interest in studying SLS algorithms on random k-SAT instances. Compared to random 3-SAT instances which have special statistical properties rendering them easy to solve, random k-SAT instances with long clauses are similar to structured ones and remain very difficult. This paper is devoted to efficient SLS algorithms for random k-SAT instances with long clauses. By combining a novel variable property subscore with the commonly used property score, we design a scoring function named comprehensive score, which is utilized to develop a new SLS algorithm called CScoreSAT. The experiments show that CScoreSAT outperforms state-ofthe- art SLS solvers, including the winners of recent SAT competitions, by one to two orders of magnitudes on large random 5-SAT and 7-SAT instances. In addition, CScoreSAT significantly outperforms its competitors on random k-SAT instances for each k = 4; 5; 6; 7 from SAT Challenge 2012, which indicates its robustness.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherAAAI Pressen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttps://www.ijcai.org/Abstract/13/080en_US
dc.relation.ispartofstudentpublicationYen_US
dc.relation.ispartofconferencenameIJCAI 2013en_US
dc.relation.ispartofconferencetitleProceedings of the 23rd International Joint Conference on Artificial Intelligenceen_US
dc.relation.ispartofdatefrom2013-08-03
dc.relation.ispartofdateto2013-08-09
dc.relation.ispartoflocationBeijing, Chinaen_US
dc.relation.ispartofpagefrom489en_US
dc.relation.ispartofpageto495en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleComprehensive Score: Towards Efficient Local Search for SAT with Long Clausesen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionPublisheden_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyright© 2013 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_US
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorCai, Shaowei
gro.griffith.authorSu, Kaile


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    Contains papers delivered by Griffith authors at national and international conferences.

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