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dc.contributor.convenorVietnam National University
dc.contributor.authorThornton, John
dc.contributor.authorPham, Nghia
dc.contributor.editorTu-Bao Ho and Zhi-Hua Zhou
dc.date.accessioned2017-05-03T12:54:34Z
dc.date.available2017-05-03T12:54:34Z
dc.date.issued2008
dc.date.modified2009-07-03T06:57:47Z
dc.identifier.refurihttp://www.jaist.ac.jp/PRICAI-08/
dc.identifier.doi10.1007/978-3-540-89197-0_38
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent36011 bytes
dc.format.extent197915 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeHeidelberg, Germany
dc.publisher.urihttp://www.jaist.ac.jp/PRICAI-08/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename10th Pacific Rim International Conference on Artificial Intelligence
dc.relation.ispartofconferencetitlePRICAI 2008: Trends in Artificial Intelligence
dc.relation.ispartofdatefrom2008-12-15
dc.relation.ispartofdateto2008-12-19
dc.relation.ispartoflocationHanoi, Vietnam
dc.rights.retentionY
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleUsing Cost Distributions to Guide Weight Decay in Local Search for SAT
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.com
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorThornton, John R.
gro.griffith.authorPham, Nghia N.


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

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