Opponent Modeling in Multiplayer Imperfect-Information Games

Sam Ganzfried, Kevin A. Wang and Max Chiswick

Papers from arXiv.org

Abstract: In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.

Date: 2022-12, Revised 2024-07
New Economics Papers: this item is included in nep-gth and nep-mic
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