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Article Dans Une Revue Artificial Intelligence Année : 2021

Epistemic GDL: A logic for representing and reasoning about imperfect information games

Guifei Jiang
  • Fonction : Auteur
  • PersonId : 1129622
Dongmo Zhang
  • Fonction : Auteur
  • PersonId : 1129623
Laurent Perrussel
Heng Zhang
  • Fonction : Auteur
  • PersonId : 1129624

Résumé

This paper proposes a logical framework for representing and reasoning about imperfect information games. We first extend Game Description Language (GDL) with the standard epistemic operators and provide it with a semantics based on the epistemic state transition model. We then demonstrate how to use the language to represent the rules of an imperfect information game and formalize common game properties as well as epistemic properties. We also show how to use the framework to reason about players' own and each others' knowledge during game playing. Furthermore, we prove that the model-checking problem of the framework is in , even though its lower bound is . These results indicate that the framework makes a good balance between expressive power and computational efficiency. Finally we provide a sound and complete axiomatic system for this logic. With action, temporal and epistemic operators, the completeness proof requires a novel combination of techniques used for completeness of dynamic logic and epistemic temporal logics. The proof theory provides a feasible tool to analyze properties of a family of games.
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Dates et versions

hal-03594780 , version 1 (02-03-2022)

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Citer

Guifei Jiang, Dongmo Zhang, Laurent Perrussel, Heng Zhang. Epistemic GDL: A logic for representing and reasoning about imperfect information games. Artificial Intelligence, 2021, Special issue: SI: Epistemic Planning, 294, pp.103453. ⟨10.1016/j.artint.2021.103453⟩. ⟨hal-03594780⟩
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