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dc.contributor.authorAkgun, Ozgur
dc.contributor.authorAttieh, Saad Wasim A
dc.contributor.authorGent, Ian Philip
dc.contributor.authorJefferson, Christopher Anthony
dc.contributor.authorMiguel, Ian James
dc.contributor.authorNightingale, Peter William
dc.contributor.authorSalamon, András Z.
dc.contributor.authorSpracklen, Patrick
dc.contributor.authorWetter, James Patrick
dc.contributor.editorLang, Jérôme
dc.date.accessioned2018-07-23T10:30:05Z
dc.date.available2018-07-23T10:30:05Z
dc.date.issued2018-07-13
dc.identifier.citationAkgun , O , Attieh , S W A , Gent , I P , Jefferson , C A , Miguel , I J , Nightingale , P W , Salamon , A Z , Spracklen , P & Wetter , J P 2018 , A framework for constraint based local search using ESSENCE . in J Lang (ed.) , Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence , pp. 1242-1248 , 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence , Stockholm , Sweden , 13/07/18 . https://doi.org/10.24963/ijcai.2018/173en
dc.identifier.citationconferenceen
dc.identifier.isbn9780999241127
dc.identifier.otherPURE: 253026207
dc.identifier.otherPURE UUID: 413b9d13-24cf-4826-b5ea-1a130eb96159
dc.identifier.otherScopus: 85055695674
dc.identifier.otherORCID: /0000-0002-5052-8634/work/46761084
dc.identifier.otherORCID: /0000-0001-9519-938X/work/46761170
dc.identifier.otherORCID: /0000-0002-1415-9712/work/46761209
dc.identifier.otherORCID: /0000-0003-2979-5989/work/60887581
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281477
dc.identifier.otherWOS: 000764175401053
dc.identifier.urihttps://hdl.handle.net/10023/15636
dc.descriptionFunding: UK Engineering & Physical Sciences Research Council (EPSRC) grants EP/P015638/1and EP/P026842/1.en
dc.description.abstractStructured Neighbourhood Search (SNS) is a framework for constraint-based local search for problems expressed in the Essence abstract constraint specification language. The local search explores a structured neighbourhood, where each state in the neighbourhood preserves a high level structural feature of the problem. SNS derives highly structured problem-specific neighbourhoods automatically and directly from the features of the ESSENCE specification of the problem. Hence, neighbourhoods can represent important structural features of the problem, such as partitions of sets, even if that structure is obscured in the low-level input format required by a constraint solver. SNS expresses each neighbourhood as a constrained optimisation problem, which is solved with a constraint solver. We have implemented SNS, together with automatic generation of neighbourhoods for high level structures, and report high quality results for several optimisation problems.
dc.format.extent7
dc.language.isoeng
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.ispartofProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligenceen
dc.rightsCopyright © 2018, IJCAI. This work has been made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at https://doi.org/10.24963/ijcai.2018/173en
dc.subjectConstraints and SAT: constraint satisfactionen
dc.subjectConstraints and SAT: modeling; formulationen
dc.subjectConstraints and SAT: constraint ptimisationen
dc.subjectConstraints and SAT: Constraints: solvers and toolsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectT Technologyen
dc.subjectNDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject~DC~en
dc.subject.lccQA75en
dc.subject.lccQA76en
dc.subject.lccTen
dc.titleA framework for constraint based local search using ESSENCEen
dc.typeConference itemen
dc.contributor.sponsorEPSRCen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.identifier.doihttps://doi.org/10.24963/ijcai.2018/173
dc.identifier.grantnumberEP/P026842/1en


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