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
Link To Document :
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