Show simple item record

dc.contributor.authorPham, Nghiaen_US
dc.contributor.authorThornton, Johnen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorFrédéric Benhamouen_US
dc.date.accessioned2017-05-03T12:54:28Z
dc.date.available2017-05-03T12:54:28Z
dc.date.issued2006en_US
dc.date.modified2007-09-26T06:04:02Z
dc.identifier.refurihttp://www.sciences.univ-nantes.fr/cp06/en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13101
dc.description.abstractIn this paper, we investigate how an IA network can be effectively encoded into the SAT domain.We propose two basic approaches to modelling an IA network as a CSP: one represents the relations between intervals as variables and the other represents the relations between end-points of intervals as variables. By combining these two approaches with three different SAT encoding schemes, we produced six encoding schemes for converting IA to SAT. These encodings were empirically studied using randomly generated IA problems of sizes ranging from 20 to 100 nodes. A general conclusion we draw from these experimental results is that encoding IA into SAT produces better results than existing approaches. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT encoding, but, surprisingly, the location of the hard region varies according to the encoding scheme. Our results also show a fixed performance ranking order over the various encoding schemes.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent195731 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer-Verlagen_US
dc.publisher.placeBerlinen_US
dc.publisher.urihttp://www.springer.com/east/home/generic/search/results?SGWID=5-40109-22-173681505-0en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename12th International Conference on the Principles and Practice of Constraint Programming (CP 2006)en_US
dc.relation.ispartofconferencetitlePrinciples and Practice of Constraint Programming - CP 2006en_US
dc.relation.ispartofdatefrom2006-09-24en_US
dc.relation.ispartofdateto2006-09-29en_US
dc.relation.ispartoflocationNantes, Franceen_US
dc.rights.retentionNen_AU
dc.subject.fieldofresearchcode280213en_US
dc.titleTowards an Efficient SAT Encoding for Temporal Reasoningen_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.rights.copyrightCopyright 2006 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_AU
gro.date.issued2006
gro.hasfulltextFull Text


Files in this item

This item appears in the following Collection(s)

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

Show simple item record