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Permanent link (DOI): https://doi.org/10.7939/R35N5V

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Computing Robust Counter-Strategies Open Access

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Author or creator
Johanson, Michael
Zinkevich, Martin
Bowling, Michael
Additional contributors
Subject/Keyword
Computer Games
poker
Type of item
Computing Science Technical Report
Computing science technical report ID
TR07-15
Language
English
Place
Time
Description
Technical report TR07-15. 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.
Date created
2007
DOI
doi:10.7939/R35N5V
License information
Creative Commons Attribution 3.0 Unported
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