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Computing Robust Counter-Strategies

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Adaptation to other initially unknown agents often requires computing an effective counter-strategy. In the Bayesian paradigm, one must find a good counter-strategy 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 counter-strategies, while still exploiting a suspected tendency. We also compose the generated strategies in an experts algorithm showing a dramatic improvement in performance over using simple best responses.

Citation

M. Johanson, M. Zinkevich, M. Bowling. "Computing Robust Counter-Strategies". Neural Information Processing Systems (NIPS), (ed: J.C. Platt, D. Koller, Y. Singer, S. Roweis), pp 721--728, December 2007.

Keywords: game theory
Category: In Conference

BibTeX

@incollection{Johanson+al:NIPS07,
  author = {Michael Johanson and Martin Zinkevich and Michael Bowling},
  title = {Computing Robust Counter-Strategies},
  Editor = {J.C. Platt, D. Koller, Y. Singer, S. Roweis},
  Pages = {721--728},
  booktitle = {Neural Information Processing Systems (NIPS)},
  year = 2007,
}

Last Updated: August 19, 2009
Submitted by Michael Johanson

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