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dc.contributor.authorPham, Duc Nghia
dc.contributor.authorThornton, John
dc.contributor.authorSattar, Abdul
dc.contributor.editorC. R. Perault and A.G. Cohn
dc.date.accessioned2017-05-03T12:54:36Z
dc.date.available2017-05-03T12:54:36Z
dc.date.issued2008
dc.date.modified2010-01-13T06:52:04Z
dc.identifier.issn0004-3702
dc.identifier.doi10.1016/j.artint.2008.06.003
dc.identifier.urihttp://hdl.handle.net/10072/26367
dc.description.abstractRepresenting and reasoning about time dependent information is a key research issue in many areas of computer science and artificial intelligence. One of the best known and widely used formalisms for representing interval-based qualitative temporal information is Allen's interval algebra (IA). The fundamental reasoning task in IA is to find a scenario that is consistent with the given information. This problem is in general NP-complete. In this paper, we investigate how an interval-based representation, or IA network, can be encoded into a propositional formula of Boolean variables and/or predicates in decidable theories. Our task is to discover whether satisfying such a formula can be more efficient than finding a consistent scenario for the original problem. There are two basic approaches to modelling an IA network: one represents the relations between intervals as variables and the other represents the end-points of each interval as variables. By combining these two approaches with three different Boolean satisfiability(SAT) encoding schemes, we produced six encoding schemes for converting IA to SAT. In addition, we also showed how IA networks can be formulated into satisfiability modulo theories (SMT) formulae based on the quantifier-free integer difference logic (QF-IDL). 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. More specifically, we show that the new point-based 1-D support SAT encoding of IA produces consistently better results than the other alternatives considered. In comparison with the six different SAT encodings, the SMT encoding came fourth after the point-based and interval-based 1-D support schemes and the point-based direct scheme. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT or SMT 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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent497888 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeAmsterdam, Netherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1752
dc.relation.ispartofpageto1782
dc.relation.ispartofissue15
dc.relation.ispartofjournalArtificial Intelligence
dc.relation.ispartofvolume172
dc.rights.retentionY
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode4613
dc.subject.fieldofresearchcode5204
dc.titleModelling and Solving Temporal Reasoning as Propositional Satisfiability
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2008 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul


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