AAAI 2024technical2 citations
Opponent-Model Search in Games with Incomplete Information
Junkang Li, Bruno Zanuttini, Véronique Ventos
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.
BibTeX
@article{Li_Zanuttini_Ventos_2024, title={Opponent-Model Search in Games with Incomplete Information}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28844}, DOI={10.1609/aaai.v38i9.28844}, abstractNote={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.}, number={9}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Junkang and Zanuttini, Bruno and Ventos, Véronique}, year={2024}, month={Mar.}, pages={9840-9847} }