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The development of competitive artificial Poker playing agents has proven tobe a challenge, because agents must deal with unreliable information anddeception which make it essential to model the opponents in order to achievegood results. This paper presents a methodology to develop opponent modelingtechniques for Poker agents. The approach is based on applying clusteringalgorithms to a Poker game database in order to identify player types based ontheir actions. First, common game moves were identified by clustering allplayers\' moves. Then, player types were defined by calculating the frequencywith which the players perform each type of movement. With the given dataset, 7different types of players were identified with each one having at least onetactic that characterizes him. The identification of player types may improvethe overall performance of Poker agents, because it helps the agents to predictthe opponent\'s moves, by associating each opponent to a distinct cluster.

Identifying Player\'s Strategies in No Limit Texas Hold\'em Poker through the Analysis of Individual Moves