Using Stereotypes to Improve Early-Match Poker Play

  • Robert Layton
  • Peter Vamplew
  • Chris Turville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)


Agent modelling is a critical aspect of many artificial intelligence systems. Many different techniques are used to learn the tendencies of another agent, though most suffer from a slow learning time. The research proposed in this paper examines stereotyping as a method to improve the learning time of poker playing agents. Poker is a difficult domain for opponent modelling due to its hidden information, stochastic elements and complex strategies. However, the literature suggests there are clusters of similar poker strategies, making it an ideal environment to test the effectiveness of stereotyping. This paper presents a method for using stereotyping in a poker bot, and shows that stereotyping improves performance in early-match play in many scenarios.


opponent modelling agent modelling stereotypes poker games 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Robert Layton
    • 1
  • Peter Vamplew
    • 1
  • Chris Turville
    • 1
  1. 1.School of ITMSUniversity of BallaratBallaratAustralia

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