A framework for constraint based local search using ESSENCE
- Submitting institution
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University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 255918267
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2018/173
- Title of conference / published proceedings
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- First page
- 1242
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2018
- 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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8
- 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
- This paper solves a long-standing problem in application of local search methods to constraint models. The problem is to find meaningful neighbourhoods around previously found low-quality solutions. The best neighbourhoods align with the structure of the problem being solved, but existing methods of neighbourhood construction could not achieve this. We take advantage of the model-based methodology of Constraint Programming to produce neighbourhoods from a declarative specification of the problem automatically. The result is the revolutionary ability to construct a specialist local-search method for a problem from a declarative description of it. The result has been increased interest in constraint based local search methods.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
