Effective Short-Term Opponent Exploitation in Simplified Poker
- Bret Hoehn, Department of Computing Science, University of Alberta
- Finnegan Southey
- Robert Holte, Department of Computing Science, University of Alberta
- Valeriy Bulitko, Centre for Science, Athabasca University
Uncertainty in poker stems from two key sources, the shufed deck and an adversary whose strategy is unknown. One approach is to nd a pessimistic game theoretic solution (i.e. a Nash equilibrium), but human players have idiosyncratic weaknesses that can be exploited if a model of their strategy can be learned by observing their play. However, games against humans last for at most a few hundred hands so learning must be fast to be effective. We explore two approaches to opponent modelling in the context of Kuhn poker, a small game for which game theoretic solutions are known. Parameter estimation and expert algorithms are both studied. Experiments demonstrate that, even in this small game, convergence to maximally exploitive solutions in a small number of hands is impractical, but that good (i.e. better than Nash or breakeven) performance can be achieved in a short period of time. Finally, we show that amongst a set of strategies with equal game theoretic value, in particular the set of Nash equilibrium strategies, some are preferable because they speed learning of the opponent's strategy by exploring it more effectively.
Citation
B. Hoehn, F. Southey, R. Holte, V. Bulitko. "Effective Short-Term Opponent Exploitation in Simplified Poker". National Conference on Artificial Intelligence (AAAI), Pittsburgh, pp 783-788, July 2005.| Keywords: | short-term, exploitation, simplified, poker, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Hoehn+al:AAAI05,
author = {Bret Hoehn and Finnegan Southey and Robert Holte and Valeriy
Bulitko},
title = {Effective Short-Term Opponent Exploitation in Simplified Poker},
Pages = {783-788},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2005,
}Last Updated: June 04, 2007Submitted by Staurt H. Johnson
