Computing Robust Counter-Strategies

Adaptation to other initially unknown agents often requires computing an effective counter-strategy. In the Bayesian paradigm, one must find a good counterstrategy to the inferred posterior of the other agents’ behavior. In the experts paradigm, one may want to choose experts that are good counter-strategies to the other agents’ expected behavior. In this paper we introduce a technique for computing robust counter-strategies for adaptation in multiagent scenarios under a variety of paradigms. The strategies can take advantage of a suspected tendency in the decisions of the other agents, while bounding the worst-case performance when the tendency is not observed. The technique involves solving a modified game, and therefore can make use of recently developed algorithms for solving very large extensive games. We demonstrate the effectiveness of the technique in two-player Texas Hold’em. We show that the computed poker strategies are substantially more robust than best response c...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Michael Johanson, Martin Zinkevich, Michael H. Bowling
• Keywords: Bayesian Paradigm, Experts, Information Technology, NIPS 2007, Unknown Agents