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dc.contributor.convenorAmedeo Cesta and Ioannis Refanidis
dc.contributor.authorRobinson, N
dc.contributor.authorGretton, C
dc.contributor.authorPham, DN
dc.contributor.authorSattar, A
dc.contributor.editorGerevini, Alfonso
dc.contributor.editorHowe, Adele
dc.contributor.editorCesta, Amedeo
dc.contributor.editorRefanidis, Ioannis
dc.date.accessioned2017-05-03T14:38:43Z
dc.date.available2017-05-03T14:38:43Z
dc.date.issued2009
dc.date.modified2010-10-13T10:03:40Z
dc.identifier.isbn9781577354062
dc.identifier.refurihttp://icaps09.icaps-conference.org/
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAAAI Press
dc.publisher.placeMenlo Park, California
dc.publisher.urihttp://icaps09.icaps-conference.org/
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameInternational Conference on Automated Planning and Scheduling (ICAPS-09)
dc.relation.ispartofconferencetitleICAPS 2009 - Proceedings of the 19th International Conference on Automated Planning and Scheduling
dc.relation.ispartofdatefrom2009-09-19
dc.relation.ispartofdateto2009-09-23
dc.relation.ispartoflocationThessaloniki, Greece
dc.relation.ispartofpagefrom281
dc.relation.ispartofpageto288
dc.rights.retentionY
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleSAT-Based Parallel Planning Using a Split Representation of Actions
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.date.issued2009
gro.hasfulltextNo Full Text
gro.griffith.authorSattar, Abdul


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

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