Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Epistemic GDL: A Logic for Representing and Reasoning about Imperfect Information Games / 1138
Guifei Jiang, Dongmo Zhang, Laurent Perrussel, Heng Zhang

This paper proposes a logical framework for representing and reasoning about imperfect information games. We first extend the 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 its epistemic properties. We also show how to use the framework to reason about player's own as well as other players' knowledge during game playing. Finally we prove that the model-checking problem of the framework is in Δ2p, which is the lowest among the existing similar frameworks, even though its lower bound is Θ2p. These results indicate that the framework makes a good balance between expressive power and computational efficiency.