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dc.contributor.authorGhooshchi, Nina Ghanbari
dc.contributor.authorNamazi, Majid
dc.contributor.authorNewton, MA Hakim
dc.contributor.authorSattar, Abdul
dc.date.accessioned2018-07-04T01:30:18Z
dc.date.available2018-07-04T01:30:18Z
dc.date.issued2017
dc.identifier.issn1076-9757
dc.identifier.doi10.1613/jair.5378
dc.identifier.urihttp://hdl.handle.net/10072/343681
dc.description.abstractWe describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don’t cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-theart constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using don’t cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP’s efficiency over the original PaP2 running on SICS
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAAAI Press
dc.relation.ispartofpagefrom905
dc.relation.ispartofpageto966
dc.relation.ispartofjournalJournal of Artificial Intelligence Research
dc.relation.ispartofvolume58
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchApplied Mathematics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0102
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode1702
dc.titleEncoding Domain Transitions for Constraint-based Planning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Institute for Integrated and Intelligent Systems
gro.rights.copyright© 2017 A I Access Foundation, Inc. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorNewton, MAHakim A.
gro.griffith.authorGhanbari Ghooshchi, Nina
gro.griffith.authorNamazi, Majid


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