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dc.contributor.convenorAmedeo Cesta and Ioannis Refanidisen_AU
dc.contributor.authorRobinson, Nathanen_US
dc.contributor.authorGretton, Charlesen_US
dc.contributor.authorPham, Nghiaen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorAlfonso Gerevini, Adele Howe, Amedeo Cesta and Ioannis Refanidisen_US
dc.date.accessioned2017-05-03T14:38:43Z
dc.date.available2017-05-03T14:38:43Z
dc.date.issued2009en_US
dc.date.modified2010-10-13T10:03:40Z
dc.identifier.refurihttp://icaps09.icaps-conference.org/en_AU
dc.identifier.urihttp://hdl.handle.net/10072/31985
dc.description.abstractPlanning based on propositional SAT(isfiability) is a powerful approach to computing step-optimal plans given a parallel execution semantics. In this setting: (i) a solution plan must be minimal in the number of plan steps required, and (ii) non-conflicting actions can be executed instantaneously in parallel at a plan step. Underlying SAT-based approaches is the invocation of a decision procedure on a SAT encoding of a bounded version of the problem. A fundamental limitation of existing approaches is the size of these encodings. This problem stems from the use of a direct representation of actions - i.e. each action has a corresponding variable in the encoding. A longtime goal in planning has been to mitigate this limitation by developing a more compact split - also termed lifted - representation of actions in SAT encodings of parallel step-optimal problems. This paper describes such a representation. In particular, each action and each parallel execution of actions is represented uniquely as a conjunct of variables. Here, each variable is derived from action pre and post-conditions. Because multiple actions share conditions, our encoding of the planning constraints is factored and relatively compact. We find experimentally that our encoding yields a much more efficient and scalable planning procedure over the state-of-the-art in a large set of planning benchmarks.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAAAI Pressen_US
dc.publisher.placeMenlo Park, Californiaen_US
dc.publisher.urihttp://icaps09.icaps-conference.org/en_AU
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofconferencenameInternational Conference on Automated Planning and Scheduling (ICAPS-09)en_US
dc.relation.ispartofconferencetitleProceedings of the Nineteenth International Conference on Automated Planning and Scheduling (ICAPS-09)en_US
dc.relation.ispartofdatefrom2009-09-19en_US
dc.relation.ispartofdateto2009-09-23en_US
dc.relation.ispartoflocationThessaloniki, Greeceen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleSAT-Based Parallel Planning Using a Split Representation of Actionsen_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.date.issued2009
gro.hasfulltextNo Full Text


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

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