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dc.contributor.convenorVietnam National Universityen_AU
dc.contributor.authorThornton, Johnen_US
dc.contributor.authorPham, Nghiaen_US
dc.contributor.editorTu-Bao Ho and Zhi-Hua Zhouen_US
dc.date.accessioned2017-05-03T12:54:34Z
dc.date.available2017-05-03T12:54:34Z
dc.date.issued2008en_US
dc.date.modified2009-07-03T06:57:47Z
dc.identifier.refurihttp://www.jaist.ac.jp/PRICAI-08/en_AU
dc.identifier.doi10.1007/978-3-540-89197-0_38en_AU
dc.identifier.urihttp://hdl.handle.net/10072/23559
dc.description.abstractAlthough clause weighting local search algorithms have produced some of the best results on a range of challenging satisfiability (SAT) benchmarks, this performance is dependent on the careful hand-tuning of sensitive parameters. When such hand-tuning is not possible, clause weighting algorithms are generally outperformed by self-tuning WalkSAT-based algorithms such as AdaptNovelty+ and AdaptG2WSAT. In this paper we investigate tuning the weight decay parameter of two clause weighting algorithms using the statistical properties of cost distributions that are dynamically accumulated as the search progresses. This method selects a parameter setting both according to the speed of descent in the cost space and according to the shape of the accumulated cost distribution, where we take the shape to be a predictor of future performance. In a wide ranging empirical study we show that this automated approach to parameter tuning can outperform the default settings for two state-of-the-art algorithms that employ clause weighting (PAWS and gNovelty+). We also show that these self-tuning algorithms are competitive with three of the best-known self-tuning SAT local search techniques: RSAPS, AdaptNovelty+ and AdaptG2WSAT.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent36011 bytes
dc.format.extent197915 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeHeidelberg, Germanyen_US
dc.publisher.urihttp://www.jaist.ac.jp/PRICAI-08/en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename10th Pacific Rim International Conference on Artificial Intelligenceen_US
dc.relation.ispartofconferencetitlePRICAI 2008: Trends in Artificial Intelligenceen_US
dc.relation.ispartofdatefrom2008-12-15en_US
dc.relation.ispartofdateto2008-12-19en_US
dc.relation.ispartoflocationHanoi, Vietnamen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleUsing Cost Distributions to Guide Weight Decay in Local Search for SATen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2008 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_AU
gro.date.issued2008
gro.hasfulltextFull Text


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