A Co-Evolutionary System for a Development of Strategies in Poker Game

Abstract

We propose a co-evolutionary system which reciprocally develops player's strategies. As an experimental environment of the game, a poker game by four players. 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.