2022/12/12 by Ganzfried, Sam, Wang, Kevin A., Chiswick, Max · 1 citation
#Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Economics and business #Multiagent Systems (cs.MA) #Theoretical Economics (econ.TH)
paper · doi:10.48550/arxiv.2212.06027
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.