• DocumentCode
    3546910
  • Title

    A statistical exploitation module for Texas Hold´em: And it´s benefits when used with an approximate nash equilibrium strategy

  • Author

    Norris, Kevin ; Watson, Ian

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Auckland, Auckland, New Zealand
  • fYear
    2013
  • fDate
    11-13 Aug. 2013
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    An approximate Nash equilibrium strategy is difficult for opponents of all skill levels to exploit, but it is not able to exploit opponents. Opponent modeling strategies on the other hand provide the ability to exploit weak players, but have the disadvantage of being exploitable to strong players. We examine the effects of combining an approximate Nash equilibrium strategy with an opponent based strategy. We present a statistical exploitation module that is capable of adding opponent based exploitation to any base strategy for playing No Limit Texas Hold´em. This module is built to recognize statistical anomalies in the opponent´s play and capitalize on them through the use of expert designed statistical exploitations. Expert designed statistical exploitations ensure that the addition of the module does not increase the exploitability of the base strategy. The merging of an approximate Nash equilibrium strategy with the statistical exploitation module has shown promising results in our initial experiments against a range of static opponents with varying exploitabilities. It could lead to a champion level player once the module is improved to deal with dynamic opponents.
  • Keywords
    computer games; game theory; No Texas Holdem game; approximate Nash equilibrium strategy; dynamic opponents; opponent based strategy; opponent modeling strategies; static opponents; statistical anomalies; statistical exploitation module; Approximation methods; Communities; Computational modeling; Games; History; Nash equilibrium; Radiation detectors; Texas Hold´em; artificial intelligence; exploitation; game AI; nash equilibrium; opponent modeling; poker;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Games (CIG), 2013 IEEE Conference on
  • Conference_Location
    Niagara Falls, ON
  • ISSN
    2325-4270
  • Print_ISBN
    978-1-4673-5308-3
  • Type

    conf

  • DOI
    10.1109/CIG.2013.6633614
  • Filename
    6633614