Eiter, T., Geibinger, T., Higuera, N., Musliu, N., Oetsch, J., & Stepanova, D. (2022). ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser. In G. Kern-Isberner, G. Lackemeyer, & T. Meyer (Eds.), Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning — Applications and Systems (pp. 565–569). IJCAI Organization. https://doi.org/10.24963/kr.2022/58
E192-03 - Forschungsbereich Knowledge Based Systems
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Published in:
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning — Applications and Systems
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ISBN:
978-1-956792-01-0
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Date (published):
2022
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Event name:
19th International Conference on Principles of Knowledge Representation and Reasoning
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Event date:
31-Jul-2022 - 5-Aug-2022
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Event place:
Haifa, Israel
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Number of Pages:
5
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Publisher:
IJCAI Organization
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Keywords:
Logic Programming; answer set programming; Applications of KR
en
Abstract:
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.
