Attieh, Saad Wasim A, Dang, Nguyen, Jefferson, Christopher et al. (2 more authors) (2019) Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence , pp. 1056-1063.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019, IJCAI. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 12 Jul 2019 14:10 |
Last Modified: | 16 Oct 2024 11:03 |
Published Version: | https://doi.org/10.24963/ijcai.2019/148 |
Status: | Published |
Publisher: | International Joint Conferences on Artificial Intelligence |
Identification Number: | 10.24963/ijcai.2019/148 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148517 |