A Coevolutionary System for Development of Strategies in Poker Game

We propose a coevolutionary system which reciprocally develops player´s strategies in two player games. The game environment is the seven stud poker that is a complex real- world game of imperfect information. In our system, the players decide their actions based on a self-learning by Classifier Systems and then make the strategies more complex and excellent. We analyze dynamics of the evolution of the player´s strategies and show the learning process of reciprocating skills of players.