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dc.contributor.authorGretton, Charlesen_US
dc.contributor.authorPham, Nghiaen_US
dc.contributor.authorRobinson, Nathanen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorDaniel Bryce, Mausam, Sungwook Yoonen_US
dc.date.accessioned2017-04-04T18:53:58Z
dc.date.available2017-04-04T18:53:58Z
dc.date.issued2008en_US
dc.date.modified2009-04-26T06:47:49Z
dc.identifier.refurihttp://icaps08.cecs.anu.edu.auen_AU
dc.identifier.doihttp://icaps08.cecs.anu.edu.auen_AU
dc.identifier.urihttp://hdl.handle.net/10072/22578
dc.description.abstractRecent times have seen the development of a number of planners that exploit advances in SAT(is?ability) solving technology to achieve good performance. In that spirit we develop the approximate contingent planner PSLSPLAN. Our approach is based on a stochastic local search procedure for solving stochastic SAT (SSAT) representations of probabilistic planning problems. PSLSPLAN ?rst constructs an SSAT representation of the n-time step probabilistic plangraph for the problem at hand. It then iteratively calls a stochastic local search procedure to ?nd a linear plan (sequence of actions) which achieves the goal (i.e. satisfies the SSAT formula) with non-zero probability. Linear plans thus generated are merged to create a single contingent plan. Successive iterations progress from deciding the outcomes of stochastic actions in order to ?nd a linear plan quickly, to sampling the outcomes of actions. Consequently, PSLSPLAN efficiently ?nds a linear plan which logically achieves the goal. Over time it re?nes its contingent plan for likely scenarios. We empirically evaluate PSLSPLAN on benchmarks from the probabilistic track of the 5th International Planning Competition.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent193163 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAAAI Pressen_US
dc.publisher.placeMenlo Park Californiaen_US
dc.publisher.urihttp://www.aaai.org/Press/press.phpen_AU
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofconferencenameThe Eighteenth International Conference on Automated Planning and Scheduling - Workshop 5en_US
dc.relation.ispartofconferencetitleEighteenth International Conference on Automated Planning and Scheduling - Workshop on A Reality Check for Planning and Scheduling Under Uncertaintyen_US
dc.relation.ispartofdatefrom2008-09-14en_US
dc.relation.ispartofdateto2008-09-18en_US
dc.relation.ispartoflocationSydney, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280213en_US
dc.titlePropositional Probabilistic Planning-as-Satisfiability using Stochastic Local Searchen_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 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Use hypertext link for access to conference website.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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

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