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dc.contributor.authorBinnewies, Sebastian
dc.contributor.authorZhuang, Zhiqiang
dc.contributor.authorWang, Kewen
dc.contributor.editorBlai Bonet, Sven Koenig
dc.date.accessioned2018-03-11T23:27:19Z
dc.date.available2018-03-11T23:27:19Z
dc.date.issued2015
dc.identifier.isbn9781577357001
dc.identifier.urihttp://hdl.handle.net/10072/123473
dc.description.abstractThe recent years have seen several proposals aimed at placing the revision of logic programs within the belief change frameworks established for classical logic. A crucial challenge of this task lies in the nonmonotonicity of standard logic programming semantics. Existing approaches have thus used the monotonic characterisation via SE-models to develop semantic revision operators, which however neglect any syntactic information, or reverted to a syntax-oriented belief base approach altogether. In this paper, we bridge the gap between semantic and syntactic techniques by adapting the idea of a partial meet construction from classical belief change. This type of construction allows us to define new model-based operators for revising as well as contracting logic programs that preserve the syntactic structure of the programs involved. We demonstrate the rationality of our operators by testing them against the classic AGM or alternative belief change postulates adapted to the logic programming setting. We further present an algorithm that reduces the partial meet revision or contraction of a logic program to performing revision or contraction only on the relevant subsets of that program.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.publisher.placeUnited States
dc.publisher.urihttps://aaai.org/Conferences/AAAI/aaai15.php
dc.relation.ispartofconferencename29th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence
dc.relation.ispartofconferencetitlePROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
dc.relation.ispartofdatefrom2015-01-25
dc.relation.ispartofdateto2015-01-30
dc.relation.ispartoflocationAustin, TX
dc.relation.ispartofpagefrom1439
dc.relation.ispartofpageto1445
dc.relation.ispartofvolume2
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titlePartial Meet Revision and Contraction in Logic Programs
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2015 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorWang, Kewen
gro.griffith.authorBinnewies, Sebastian
gro.griffith.authorZhuang, Zhiqiang


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

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