  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.
