Automated Approach for Solving Infinite-state Polynomial Reachability Games

TL;DR

This paper introduces a novel automated method for solving infinite-state polynomial reachability games, providing a sound, semi-complete algorithm with formal correctness witnesses, capable of handling challenging examples like the Cinderella-Stepmother game.

Contribution

It proposes ranking certificates as proof rules and an automated algorithm for polynomial reachability games, advancing the state-of-the-art in infinite-state game solving.

Findings

Successfully solved the Cinderella-Stepmother game with arbitrary precision.

The algorithm is sound, semi-complete, and runs in sub-exponential time.

Demonstrated effectiveness on challenging literature examples.

Abstract

Reachability games are two-player games played on a graph, where the objective of $\texttt{REACH}$ player is to reach the target set whereas the objective of $\texttt{SAFE}$ player is to stay away from the target set. Reachability games have important applications in artificial intelligence and reactive synthesis, and many of these applications give rise to infinite-state reachability games. In this paper, we study turn-based reachability games on infinite-state graphs defined over valuations of a finite set of real variables. We consider the problem of determining the existence of and computing a winning strategy for $\texttt{REACH}$ player. Our contributions are twofold. First, we propose ranking certificates for reachability games, a sound and complete proof rule for proving that $\texttt{REACH}$ player has a winning strategy from the specified initial state. Second, we consider polynomial reachability games, where transitions and objectives are described by polynomial constraints over real variables, and propose a fully automated algorithm for computing a winning strategy for $\texttt{REACH}$ player together with a formal correctness witness in the form of a ranking certificate. The algorithm is sound, semi-complete, and runs in sub-exponential time. Our experiments demonstrate the ability of our method to solve challenging examples from the literature that were out of the reach of existing methods. Specifically, for the classical Cinderella-Stepmother game, we are able to compute an optimal winning strategy for an arbitrary precision parameter for the first time.