Identifying Playerś Strategies in No Limit Texas Holdém Poker through the Analysis of Individual Moves
This work addresses the problem of unreliable information and deception in Poker for AI agents, though it appears incremental as it applies existing clustering techniques to a specific domain.
The paper tackled the challenge of opponent modeling in No Limit Texas Hold'em Poker by developing a methodology using clustering algorithms on a game database to identify player types based on their actions, resulting in the identification of 7 distinct player types with specific tactics.
The development of competitive artificial Poker playing agents has proven to be a challenge, because agents must deal with unreliable information and deception which make it essential to model the opponents in order to achieve good results. This paper presents a methodology to develop opponent modeling techniques for Poker agents. The approach is based on applying clustering algorithms to a Poker game database in order to identify player types based on their actions. First, common game moves were identified by clustering all players\' moves. Then, player types were defined by calculating the frequency with which the players perform each type of movement. With the given dataset, 7 different types of players were identified with each one having at least one tactic that characterizes him. The identification of player types may improve the overall performance of Poker agents, because it helps the agents to predict the opponentś moves, by associating each opponent to a distinct cluster.