Solving First-Order Fixed-Point Logics via a Least-to-Greatest Transformation Based on Game Semantics

Abstract

Fixed-point logics provide an expressive intermediate framework for reasoning about temporal properties of programs. One of the key approaches to solving their validity checking problem is via transformations from least fixed points to greatest fixed points ($μ$-to-$ν$ transformations), which generalizes a reduction from termination verification to safety verification studied in binary reachability analysis. In this paper, we introduce game-semantic interpretations of $μ$-to-$ν$ transformations. We first introduce a new $μ$-to-$ν$ transformation based on parity relations. We show that solving $μ$-to-$ν$-transformed fixed-point equation systems corresponds to finding winning strategies in the game semantics of the original fixed-point equation systems. We apply the same game-semantic framework to interpret two existing $μ$-to-$ν$ transformations, one by Kobayashi et al.\ and the other by Unno et al, and show that they admit analogous game-semantic interpretations. Furthermore, we show that the game introduced by Tsukada et al.\ corresponds to an alternative characterization of the winning condition. On the implementation side, we propose optimization techniques for efficiently solving our new $μ$-to-$ν$ transformation. We implement these techniques in a fixed-point logic solver, compare our approach with existing solvers, and demonstrate the effectiveness of the proposed optimizations through experiments.