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dc.contributor.authorRobinson, Nathan
dc.contributor.authorGretton, Charles
dc.contributor.authorPham, Nghia
dc.contributor.authorSattar, Abdul
dc.contributor.editorDaniel Bryce, Mausam, Sungwook Yoon
dc.date.accessioned2017-05-03T14:09:06Z
dc.date.available2017-05-03T14:09:06Z
dc.date.issued2008
dc.date.modified2009-04-26T06:47:49Z
dc.identifier.refurihttp://icaps08.cecs.anu.edu.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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent193163 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherAAAI Press
dc.publisher.placeMenlo Park California
dc.publisher.urihttp://www.aaai.org/Press/press.php
dc.publisher.urihttp://icaps08.cecs.anu.edu.au
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameThe Eighteenth International Conference on Automated Planning and Scheduling - Workshop 5
dc.relation.ispartofconferencetitleEighteenth International Conference on Automated Planning and Scheduling - Workshop on A Reality Check for Planning and Scheduling Under Uncertainty
dc.relation.ispartofdatefrom2008-09-14
dc.relation.ispartofdateto2008-09-18
dc.relation.ispartoflocationSydney, Australia
dc.rights.retentionY
dc.subject.fieldofresearchcode280213
dc.titlePropositional Probabilistic Planning-as-Satisfiability using Stochastic Local Search
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 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.
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorPham, Nghia N.
gro.griffith.authorRobinson, Nathan M.
gro.griffith.authorGretton, Charles


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

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