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Communication Dans Un Congrès Année : 2022

Dependency Matrices for Multiplayer Strategic Dependencies

Résumé

In multi-player games, players take their decisions on the basis of their knowledge about what other players have done, or currently do, or even, in some cases, will do. An ability to reason in games with temporal dependencies between players' decisions is a challenging topic, in particular because it involves imperfect information. In this work, we propose a theoretical framework based on dependency matrices that includes many instances of strategic dependencies in multi-player imperfect information games. For our framework to be well-defined, we get inspiration from quantified lineartime logic where each player has to label the timeline with truth values of the propositional variable she owns. We study the problem of the existence of a winning strategy for a coalition of players, show it is undecidable in general, and exhibit an interesting subclass of dependency matrices that makes the problem decidable: the class of perfect-information dependency matrices.
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Dates et versions

hal-04047617 , version 1 (27-03-2023)

Identifiants

Citer

Dylan Bellier, Sophie Pinchinat, François Schwarzentruber, François Schwarzentruber. Dependency Matrices for Multiplayer Strategic Dependencies. FSTTCS 2022, 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, Dec 2022, Madras, India. pp.162 - 177, ⟨10.4230/LIPIcs.FSTTCS.2022.31⟩. ⟨hal-04047617⟩
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