By the same authors

Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence. / Attieh, Saad Wasim A; Dang, Nguyen; Jefferson, Christopher; Miguel, Ian; Nightingale, Peter.

Proceedings of the 28th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, 2019. p. 1056-1063.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Attieh, SWA, Dang, N, Jefferson, C, Miguel, I & Nightingale, P 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. https://doi.org/10.24963/ijcai.2019/148

APA

Attieh, S. W. A., Dang, N., Jefferson, C., Miguel, I., & Nightingale, P. (2019). Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 1056-1063). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/148

Vancouver

Attieh SWA, Dang N, Jefferson C, Miguel I, Nightingale P. 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. 2019. p. 1056-1063 https://doi.org/10.24963/ijcai.2019/148

Author

Attieh, Saad Wasim A ; Dang, Nguyen ; Jefferson, Christopher ; Miguel, Ian ; Nightingale, Peter. / Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence. Proceedings of the 28th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, 2019. pp. 1056-1063

Bibtex - Download

@inproceedings{4550a24e3bab44f8ac01ce38e75e44b1,
title = "Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence",
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.",
author = "Attieh, {Saad Wasim A} and Nguyen Dang and Christopher Jefferson and Ian Miguel and Peter Nightingale",
note = "{\circledC} 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.",
year = "2019",
month = "8",
day = "10",
doi = "10.24963/ijcai.2019/148",
language = "English",
pages = "1056--1063",
booktitle = "Proceedings of the 28th International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence

AU - Attieh, Saad Wasim A

AU - Dang, Nguyen

AU - Jefferson, Christopher

AU - Miguel, Ian

AU - Nightingale, Peter

N1 - © 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.

PY - 2019/8/10

Y1 - 2019/8/10

N2 - 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.

AB - 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.

U2 - 10.24963/ijcai.2019/148

DO - 10.24963/ijcai.2019/148

M3 - Conference contribution

SP - 1056

EP - 1063

BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence

PB - International Joint Conferences on Artificial Intelligence

ER -