dc.contributor.author | Robinson, Nathan | |
dc.contributor.author | Gretton, Charles | |
dc.contributor.author | Pham, Duc Nghia | |
dc.contributor.author | Sattar, Abdul | |
dc.contributor.editor | Zhang, BT | |
dc.contributor.editor | Orgun, MA | |
dc.date.accessioned | 2017-05-03T13:11:09Z | |
dc.date.available | 2017-05-03T13:11:09Z | |
dc.date.issued | 2010 | |
dc.date.modified | 2011-03-03T07:09:58Z | |
dc.identifier.isbn | 978-3-642-15245-0 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.refuri | http://www.ourglocal.com/url/?url=http%3A%2F%2Fwww.pricai2010.org%2F | |
dc.identifier.doi | 10.1007/978-3-642-15246-7_23 | |
dc.identifier.uri | http://hdl.handle.net/10072/36746 | |
dc.description.abstract | We consider the problem of computing optimal plans for propositional planning problems with action costs. In the spirit of leveraging advances in general-purpose automated reasoning for that setting, we develop an approach that operates by solving a sequence of partial weighted MaxSAT problems, each of which corresponds to a step-bounded variant of the problem at hand. Our approach is the first SAT-based system in which a proof of cost-optimality is obtained using a MaxSAT procedure. It is also the first system of this kind to incorporate an admissible planning heuristic. We perform a detailed empirical evaluation of our work using benchmarks from a number of International Planning Competitions. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.publisher.place | Germany | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | 11th Pacific Rim International Conference on Artificial Intelligence | |
dc.relation.ispartofconferencetitle | PRICAI 2010: TRENDS IN ARTIFICIAL INTELLIGENCE | |
dc.relation.ispartofdatefrom | 2010-08-30 | |
dc.relation.ispartofdateto | 2010-09-02 | |
dc.relation.ispartoflocation | Korean Inst Informat Sci & Engn, Daegu, SOUTH KOREA | |
dc.relation.ispartofpagefrom | 231 | |
dc.relation.ispartofpagefrom | 2 pages | |
dc.relation.ispartofpageto | + | |
dc.relation.ispartofpageto | 2 pages | |
dc.relation.ispartofvolume | 6230 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Artificial intelligence not elsewhere classified | |
dc.subject.fieldofresearchcode | 460299 | |
dc.title | Partial Weighted MaxSAT for Optimal Planning | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
gro.date.issued | 2010 | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Sattar, Abdul | |