Artificial intelligence research in Texas Hold'em poker has recently mainly focused on heads-up fixed-limit games. Game theoretic methods used in the poker agents capable of playing at the very best level are not easily generalized to other forms of Texas Hold'em poker. In this paper we present a general heuristic-based approach to build a poker agent, where the betting strategy is defined by estimating the expected values of the actions. The approach is well suited for a larger number of players and it is also easily modified to suite no limit games.
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