Year: 2024
Type: conference-paper
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.
Authors: Sam Ganzfried, Kevin A. Wang, Max Chiswick
Institution: Brown University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414248496