Abstract
The ability to efficiently solve hard combinatorial optimization problems is a key prerequisite to various applications of declarative programming paradigms. Symmetries in solution candidates pose a significant challenge to modern optimization algorithms since the enumeration of such candidates might substantially reduce their optimization performance. This paper proposes a novel approach using Inductive Logic Programming (ILP) to lift symmetry-breaking constraints for optimization problems modeled in Answer Set Programming (ASP). Given an ASP encoding with optimization statements and a set of small representative instances, our method augments ground ASP programs with auxiliary normal rules enabling the identification of symmetries using existing tools, like SBASS. Then, the obtained symmetries are lifted to first-order constraints with ILP. We prove the correctness of our method and evaluate it on real-world optimization problems from the domain of automated configuration. Our experiments show significant improvements of optimization performance due to the learned first-order constraints.
Original language | English |
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Title of host publication | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
Subtitle of host publication | AAAI-23 Technical Tracks 5 |
Editors | Brian Williams, Yiling Chen, Jennifer Neville |
Publisher | AAAI Press |
Pages | 6541-6549 |
Number of pages | 9 |
ISBN (Electronic) | 9781577358800 |
Publication status | Published - 27 Jun 2023 |
Event | 37th AAAI Conference on Artificial Intelligence: AAAI 2023 - Washington DC, United States Duration: 7 Feb 2023 → 14 Feb 2023 https://aaai-23.aaai.org https://aaai.org/Conferences/AAAI-23/ |
Conference
Conference | 37th AAAI Conference on Artificial Intelligence |
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Abbreviated title | AAAI 2023 |
Country/Territory | United States |
City | Washington DC |
Period | 7/02/23 → 14/02/23 |
Internet address |
ASJC Scopus subject areas
- Artificial Intelligence