Efficient SAT-Based Minimal Model Generation Methods for Modal Logic S5

Pei Huang, Rundong Li, Minghao Liu, Feifei Ma, Jian Zhang

Abstract: Modal logic S5 is useful in various applications of artificial intelligence. In recent years, the advance in solving the satisfiability problem of S5 has allowed many large S5 formulas to be solved within a few minutes. In this context, a new challenge arises: how to generate a minimal S5 Kripke model efficiently? The minimal model generation can be useful for tasks such as model checking and debugging of logical specifications. This paper presents several efficient SAT-based methods and provides a symmetry-breaking technique for the minimal model generation problem of S5. Extensive experiments demonstrate that our methods are good at tackling many large instances and achieve state-of-the-art performances. We find that a minimal model of a large S5 formula is usually very small, and we analyze this phenomenon via a graph model. Due to this characteristic, our incremental method performs best in most cases, and we believe that it is more suitable for minimal S5 Kripke model generation.