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Reports (Research Report) Year : 2023

Opponent-model search in games with incomplete information

Bruno Zanuttini
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  • PersonId : 952903
Véronique Ventos
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  • PersonId : 1133827

Abstract

Games with incomplete information are games that model situations where players do not have common knowledge about the game they play, e.g. card games such as poker or bridge. Opponent models can be of crucial importance for decision-making in such games. We propose algorithms for computing optimal and/or robust strategies in games with incomplete information, given various types of knowledge about opponent models. As an application, we describe a framework for reasoning about an opponent's reasoning in such games, where opponent models arise naturally.
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Dates and versions

hal-04100646 , version 1 (18-05-2023)
hal-04100646 , version 2 (05-02-2024)

Identifiers

  • HAL Id : hal-04100646 , version 2

Cite

Junkang Li, Bruno Zanuttini, Véronique Ventos. Opponent-model search in games with incomplete information. GREYC CNRS UMR 6072. 2023. ⟨hal-04100646v2⟩
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