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Symbolic Verification and Strategy Synthesis for Turn-based Stochastic Games

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arxiv 2211.06141 v1 pith:BMJLTBUA submitted 2022-11-11 cs.LO

classification cs.LO
keywords gamesspecificationsstochasticsymbolicagentsclasseitherstrategies
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Stochastic games are a convenient formalism for modelling systems that comprise rational agents competing or collaborating within uncertain environments. Probabilistic model checking techniques for this class of models allow us to formally specify quantitative specifications of either collective or individual behaviour and then automatically synthesise strategies for the agents under which these specifications are guaranteed to be satisfied. Although good progress has been made on algorithms and tool support, efficiency and scalability remain a challenge. In this paper, we investigate a symbolic implementation based on multi-terminal binary decision diagrams. We describe how to build and verify turn-based stochastic games against either zero-sum or Nash equilibrium based temporal logic specifications. We collate a set of benchmarks for this class of games, and evaluate the performance of our approach, showing that it is superior in a number of cases and that strategies synthesised in a symbolic fashion can be considerably more compact.

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  1. Turn-based Multi-Agent Reinforcement Learning Model Checking

    cs.AI 2025-01 conditional novelty 4.0 of 10

    A wrapper-based method verifies PCTL properties of turn-based multi-agent reinforcement learning policies via single-policy probabilistic model checking.

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