ALASPO: An Adaptive Large-Neighbourhood ASP OptimiserShow others and affiliations
2022 (English)In: KR 2022: Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning / [ed] G. Kern-Isberner G. Lakemeyer & T. Meyer, IJCAI Organization , 2022, p. 565-569Conference paper, Published paper (Refereed)
Abstract [en]
We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set Programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. This improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks.
Place, publisher, year, edition, pages
IJCAI Organization , 2022. p. 565-569
Keywords [en]
Knowledge representation, Logic programming, Adaptive large neighborhood searches, Answer set programming, Efficient implementation, Integer constraints, Large neighbourhood, Large neighbourhood searches, Metaheuristic, Multi-shot, Neighbourhood, Optimisations, Optimization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-63559Scopus ID: 2-s2.0-85141870502ISBN: 9781956792010 (print)OAI: oai:DiVA.org:hj-63559DiVA, id: diva2:1838532
Conference
19th International Conference on Principles of Knowledge Representation and Reasoning, KR 2022, 31 July-5 August 2022, Haifa, Israel
2024-02-162024-02-162024-02-16Bibliographically approved