Date of Original Version
Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
Abstract or Table of Contents
We present a new procedure for solving large games of imperfect information. Our approach involves—somewhat counterintuitively—solving an inﬁnite approximation of the original game, then mapping the equilibrium to a strategy proﬁle in the original game. Our main algorithm exploits some qualitative model of equilibrium structure as an additional input to ﬁnd an equilibrium in continuous games. We prove that our approach is correct even if given a set of qualitative models (satisfying a technical property) of which
only some are accurate. We compute equilibria in several classes of games for which no prior algorithms have been developed. In the course of our analysis, we also develop the ﬁrst mixed-integer programming formulations for computing an epsilon-equilibrium in general multiplayer normal and extensive-form games based on the extension of our initial algorithm to the multiplayer setting, which may be of independent interest. Experiments suggest that our approach can outperform the prior state of the art, abstraction-based approaches. In addition, we demonstrate the effectiveness of our main algorithm on a subgame of limit Texas hold’em—the most
studied imperfect-information game in computer science.