Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach

  • Brien Beattie
  • Garrett Nicolai
  • David Gerhard
  • Robert J. Hilderman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4509)

Abstract

We have constructed a poker classification system which makes informed betting decisions based upon three defining features extracted while playing poker: hand value, risk, and aggressiveness. The system is implemented as a player-agent, therefore the goals of the classifier are not only to correctly determine whether each hand should be folded, called, or raised, but to win as many chips as possible from the other players. The decision space is found by evolutionary methods, starting from a data-driven initial state. Our results showed that evolving an agent from a data-driven “head-start” position resulted in the best performance over agents evolved from scratch, data-driven agents, random agents, and “always fold” agents.

Keywords

Evolution Pattern Classification No-limit Hold’em Poker 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Brien Beattie
    • 1
  • Garrett Nicolai
    • 1
  • David Gerhard
    • 1
  • Robert J. Hilderman
    • 1
  1. 1.University of Regina, Department of Computer Science, Regina, SK S4S 0A2Canada

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