Proceedings

Lifting Symmetry Breaking Constraints with Inductive Logic Programming

Alice Tarzariol, Martin Gebser, Konstantin Schekotihin

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2062-2068.

https://doi.org/10.24963/ijcai.2021/284

Keywords

  • Machine Learning: Explainable/Interpretable Machine Learning
  • Knowledge Representation and Reasoning: Leveraging Knowledge and Learning
  • Constraints and SAT: Constraints: Modeling, Solvers, Applications

Abstract

Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic. Moreover, the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances.

To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm.

Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.

Alice Tarzariol, Martin Gebser, and Konstantin Schekotihin. 2021. Lifting Symmetry Breaking Constraints with Inductive Logic Programming. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2062–2068. https://doi.org/10.24963/ijcai.2021/284
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