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dc.contributor.authorAttieh, Saad
dc.contributor.authorDang, Nguyen
dc.contributor.authorJefferson, Christopher
dc.contributor.authorMiguel, Ian
dc.contributor.authorNightingale, Peter
dc.contributor.editorKraus, Sarit
dc.date.accessioned2019-09-04T10:30:17Z
dc.date.available2019-09-04T10:30:17Z
dc.date.issued2019-08-10
dc.identifier.citationAttieh , S , Dang , N , Jefferson , C , Miguel , I & Nightingale , P 2019 , Athanor : high-level local search over abstract constraint specifications in Essence . in S Kraus (ed.) , Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) . International Joint Conferences on Artificial Intelligence , pp. 1056-1063 , Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) , Macao , China , 10/08/19 . https://doi.org/10.24963/ijcai.2019/148en
dc.identifier.citationconferenceen
dc.identifier.isbn9780999241141
dc.identifier.otherPURE: 261009387
dc.identifier.otherPURE UUID: 1cad2ad7-27c8-4475-86a9-177762ecb613
dc.identifier.othercrossref: 10.24963/ijcai.2019/148
dc.identifier.otherORCID: /0000-0002-5052-8634/work/60887286
dc.identifier.otherORCID: /0000-0003-2979-5989/work/60887544
dc.identifier.otherORCID: /0000-0002-2693-6953/work/60888374
dc.identifier.otherORCID: /0000-0002-6930-2686/work/68281438
dc.identifier.otherScopus: 85074910140
dc.identifier.urihttp://hdl.handle.net/10023/18415
dc.descriptionFunding: EPSRC grants EP/P015638/1and EP/P026842/1. Christopher Jefferson is supported by a Royal Society University Research Fellowship. This work used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).en
dc.description.abstractThis paper presents Athanor, a novel local search solver that operates on abstract constraint specifications of combinatorial problems in the Essence language. It is unique in that it operates directly on the high level, nested types in Essence, such as set of partitions or multiset of sequences, without refining such types into low level representations. This approach has two main advantages. First, the structure present in the high level types allows high quality neighbourhoods for local search to be automatically derived. Second, it allows Athanor to scale much better than solvers that operate on the equivalent, but much larger, low-level representations. The paper details how Athanor operates, covering incremental evaluation, dynamic unrolling of quantified expressions and neighbourhood construction. A series of case studies show the performance of Athanor, benchmarked against several local search solvers on a range of problem classes.
dc.format.extent8
dc.language.isoeng
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.ispartofProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)en
dc.rightsCopyright © 2019 International Joint Conferences on Artificial Intelligence. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://doi.org/10.24963/ijcai.2019/148en
dc.subjectConstraints and SAT: Constraints: solvers and toolsen
dc.subjectConstraints and SAT: Modeling;formulationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectT-NDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject~DC~en
dc.subject.lccQA75en
dc.subject.lccQA76en
dc.titleAthanor : high-level local search over abstract constraint specifications in Essenceen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Equality, Diversity & Inclusionen
dc.contributor.institutionUniversity of St Andrews.Centre for Interdisciplinary Research in Computational Algebraen
dc.contributor.institutionUniversity of St Andrews.St Andrews GAP Centreen
dc.identifier.doihttps://doi.org/10.24963/ijcai.2019/148


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