Abstract—Among all the technologies in creating a good
poker agent, estimating winning probability is a key issue. In
this paper, we propose an approach to estimating winning
probability for Texas Hold’em poker. We design a data
structure using both the observable data from the current
board and the history. A Support Vector Machine classifier is
trained and 5-fold cross-validation is employed. We create a
poker agent with some decision making strategies to compete.
Experimental results show that our method has outperformed
three other agents in precision of estimating winning
probability.
Index Terms—Opponent modeling, support vector machine,
texas hold’em poker, winning probability.
The authors are with the State Key Laboratory for Novel Software
Technology, Department of Computer Science and Technology at the
Nanjing University, Nanjing, China (e-mail: easerene@gmail.com).
Cite:Wenkai Li and Lin Shang, "Estimating Winning Probability for Texas Hold'em Poker," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 70-74, 2013.