Athanor : high-level local search over abstract constraint specifications in Essence
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This 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.
Attieh , 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.2019/148conference
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
Copyright © 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.2019/148
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).
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