A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming

• URL: http://arxiv.org/abs/2208.03095v1
• Date: Fri, 5 Aug 2022 10:50:03 GMT
• Title: A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming
• Authors: Alice Tarzariol (University of Klagenfurt)
• Abstract summary: We introduce a new model-oriented Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints. After targeting simple problems, we aim to extend our method to be applied also for advanced decision problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. 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 since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. 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 a form of machine learning called Inductive Logic Programming. After targeting simple combinatorial problems, we aim to extend our method to be applied also for advanced decision and optimization problems.

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