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Concurrent probabilistic planning in the graphplan framework

Little, Iain; Thiebaux, Sylvie

Description

We consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditions; we seek a contingency plan that maximises the probability of reaching the goal. The Graphplan framework has proven to be highly successful at solving classical planning problems, but has not previously been applied to probabilistic planning in its entirety. We present an extension of the full framework to...[Show more]

dc.contributor.authorLittle, Iain
dc.contributor.authorThiebaux, Sylvie
dc.coverage.spatialCumbria UK
dc.date.accessioned2015-12-07T22:20:54Z
dc.date.createdJune 6-10 2005
dc.identifier.isbn9781577352709
dc.identifier.urihttp://hdl.handle.net/1885/19794
dc.description.abstractWe consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditions; we seek a contingency plan that maximises the probability of reaching the goal. The Graphplan framework has proven to be highly successful at solving classical planning problems, but has not previously been applied to probabilistic planning in its entirety. We present an extension of the full framework to probabilistic domains that demonstrates a method of efficiently finding optimal contingency plans using a goal regression search. Paragraph, the resulting planner, is competitive with the state of the art, producing acyclic or cyclic plans that optionally exploit a problem's potential for concurrency.
dc.publisherAAAI Press
dc.relation.ispartofseriesInternational Conference on Automated Planning and Scheduling (ICAPS 2006)
dc.sourceProceedings of the Sixteenth International Conference on Automated Planning and Scheduling
dc.source.urihttp://www.aiconferences.org/ICAPS/2006/icaps06.html
dc.subjectKeywords: Artificial intelligence; Concurrency control; Graph theory; Optimization; Problem solving; Classical planning problems; Concurrent probabilistic planning; Cyclic plans; Probabilistic domains; Probabilistic logics
dc.titleConcurrent probabilistic planning in the graphplan framework
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2006
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.ariespublicationu8803936xPUB9
local.type.statusPublished Version
local.contributor.affiliationLittle, Iain, College of Engineering and Computer Science, ANU
local.contributor.affiliationThiebaux, Sylvie, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage263
local.bibliographicCitation.lastpage273
dc.date.updated2015-12-07T08:52:04Z
local.identifier.scopusID2-s2.0-33746077700
CollectionsANU Research Publications

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