Learning Nash equilibria by coevolving distributed classifier systems

Abstract

We consider a team of classifier systems (CSs), operating in a distributed environment of a game-theoretic model. This distributed model, a game with limited interaction, is a variant of N-person Prisoner Dilemma game. A payoff of each CS in this model depends only on its action and on actions of limited number of its neighbors in the game. CSs coevolve while competing for their payoffs. We show how such classifiers learn Nash equilibria, and what variety of behavior is generated: from pure competition to pure cooperation.