A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming

A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming

Alice Tarzariol

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5875-5876. https://doi.org/10.24963/ijcai.2022/840

Writing correct models for combinatorial problems is relatively straightforward; however, they must be efficient to be usable with instances producing many solution candidates. In this work, we aim to automatically generalise the discarding of symmetric solutions of Answer Set Programming instances, improving the efficiency of the programs with first-order constraints derived from propositional symmetry-breaking constraints.
Keywords:
Search and Optimization (SO): General
Knowledge Representation and Reasoning (KRR): General
Machine Learning (ML): General