Automatic Verification of FSA Strategies via Counterexample-Guided Local Search for Invariants

Automatic Verification of FSA Strategies via Counterexample-Guided Local Search for Invariants

Kailun Luo, Yongmei Liu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1814-1821. https://doi.org/10.24963/ijcai.2019/251

Strategy representation and reasoning has received much attention over the past years. In this paper, we consider the representation of general strategies that solve a class of (possibly infinitely many) games with similar structures, and their automatic verification, which is an undecidable problem. We propose to represent a general strategy by an FSA (Finite State Automaton) with edges labelled by restricted Golog programs. We formalize the semantics of FSA strategies in the situation calculus. Then we propose an incomplete method for verifying whether an FSA strategy is a winning strategy by counterexample-guided local search for appropriate invariants. We implemented our method and did experiments on combinatorial game and also single-agent domains. Experimental results showed that our system can successfully verify most of them within a reasonable amount of time.
Keywords:
Knowledge Representation and Reasoning: Action, Change and Causality
Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving