Athanor : high-level local search over abstract constraint specifications in Essence
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 260747417
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2019/148
- Title of conference / published proceedings
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
- First page
- 1056
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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A - Artificial Intelligence
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Constraint Programming offers techniques for solving complex decision-making and optimisation problems in a generic manner. However, useful high-level structural information about the problems is often not well exploited during the solving process. This work proposed a novel constraint-based solving approach that can overcome this limitation by directly operating on a high-level formulation of the problem. The proposed solver results in not only highly competitive performance but also greater scalability compared with the current state-of-the-art solvers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
