Using Stereotypes to Improve Early-Match Poker Play

Authors:
Robert Layton;Peter Vamplew;Chris Turville
Affiliations:
School of ITMS, University of Ballarat, Ballarat, Australia;School of ITMS, University of Ballarat, Ballarat, Australia;School of ITMS, University of Ballarat, Ballarat, Australia
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

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.