Using probabilistic knowledge and simulation to play poker
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Finding equilibria in large sequential games of imperfect information
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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CG'04 Proceedings of the 4th international conference on Computers and Games
Artificial Intelligence
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We present a Texas Hold'em poker player for limit headsup games. Our bot is designed to adapt automatically to the strategy of the opponent and is not based on Nash equilibrium computation. The main idea is to design a bot that builds beliefs on his opponent's hand. A forest of game trees is generated according to those beliefs and the solutions of the trees are combined to make the best decision. The beliefs are updated during the game according to several methods, each of which corresponding to a basic strategy. We then use an exploration-exploitation bandit algorithm, namely the UCB (Upper Confidence Bound), to select a strategy to follow. This results in a global play that takes into account the opponent's strategy, and which turns out to be rather unpredictable. Indeed, if a given strategy is exploited by an opponent, the UCB algorithm will detect it using change point detection, and will choose another one. The initial resulting program, called Brennus, participated to the AAAI'07 Computer Poker Competition in both online and equilibrium competition and ranked eight out of seventeen competitors.