Learning to Break Symmetries for Efficient Optimization in Answer Set Programming (Extended 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. The full version of 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 representative instances, our method augments ground ASP programs with auxiliary normal rules enabling the identification of symmetries by existing tools, like SBASS. Then, the obtained symmetries are lifted to first-order constraints with ILP. We show the correctness of our method and evaluate it on optimization problems from the domain of automated configuration. Our experiments show significant improvements of optimization performance due to the learned first-order constraints. The full version of this paper was presented at AAAI 2023.