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Journal Articles Journal of Artificial Intelligence Research Year : 2022

Approximating Perfect Recall when Model Checking Strategic Abilities: Theory and Applications

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Abstract

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗ , under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for AT L∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for AT L and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results.

Dates and versions

hal-03779030 , version 1 (16-09-2022)

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Cite

Francesco Belardinelli, Alessio Lomuscio, Vadim Malvone, Emily Yu. Approximating Perfect Recall when Model Checking Strategic Abilities: Theory and Applications. Journal of Artificial Intelligence Research, 2022, 73, pp.897-932. ⟨10.1613/jair.1.12539⟩. ⟨hal-03779030⟩
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