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Equilibria-Based Probabilistic Model Checking for Concurrent Stochastic Games

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Formal Methods – The Next 30 Years (FM 2019)

Abstract

Probabilistic model checking for stochastic games enables formal verification of systems that comprise competing or collaborating entities operating in a stochastic environment. Despite good progress in the area, existing approaches focus on zero-sum goals and cannot reason about scenarios where entities are endowed with different objectives. In this paper, we propose probabilistic model checking techniques for concurrent stochastic games based on Nash equilibria. We extend the temporal logic rPATL (probabilistic alternating-time temporal logic with rewards) to allow reasoning about players with distinct quantitative goals, which capture either the probability of an event occurring or a reward measure. We present algorithms to synthesise strategies that are subgame perfect social welfare optimal Nash equilibria, i.e., where there is no incentive for any players to unilaterally change their strategy in any state of the game, whilst the combined probabilities or rewards are maximised. We implement our techniques in the PRISM-games tool and apply them to several case studies, including network protocols and robot navigation, showing the benefits compared to existing approaches.

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Notes

  1. 1.

    In the case of infinite-horizon properties, this is a subgame perfect \(\varepsilon \)-SWNE.

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Acknowledgements

This work is partially supported by the EPSRC Programme Grant on Mobile Autonomy and the PRINCESS project, under the DARPA BRASS programme. We would like to thank the reviewers of an earlier version of this paper for finding a flaw in the correctness proof.

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Correspondence to Gethin Norman .

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Kwiatkowska, M., Norman, G., Parker, D., Santos, G. (2019). Equilibria-Based Probabilistic Model Checking for Concurrent Stochastic Games. In: ter Beek, M., McIver, A., Oliveira, J. (eds) Formal Methods – The Next 30 Years. FM 2019. Lecture Notes in Computer Science(), vol 11800. Springer, Cham. https://doi.org/10.1007/978-3-030-30942-8_19

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