Automated Approach for Solving Infinite-state Polynomial Reachability Games
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Abstract (arXiv)
Reachability games are two-player games played on a graph, where the objective of textttREACH player is to reach the target set whereas the objective of textttSAFE 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 textttREACH player. Our contributions are twofold. First, we propose ranking certificates for reachability games, a sound and complete proof rule for proving that textttREACH 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 textttREACH 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.
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References · 13
- 2023Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learningcited by 29
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- 2017Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contractscited by 6
- 2021GenSys: A Scalable Fixed-point Engine for Maximal Controller Synthesis over Infinite State Spacescited by 4
- 2021Causality-Based Game Solvingcited by 3
- 2021Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)cited by 3
- 2023Solving Infinite-State Games via Acceleration (Full Version)cited by 3
- 2024Localized Attractor Computations for Infinite-State Games (Full Version)cited by 3
- 2024PolyQEnt: A Polynomial Quantified Entailment Solvercited by 3
- 2020Parameterized Synthesis with Safety Propertiescited by 2
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