Learning classifier systems in multi-agent environments

The paper is devoted to the problem of learning decision policies in multi-agent games. We describe a general framework for studying games of intelligent agents, extending the basic model of games with limited interactions, and its specific realization based on learning classifier systems. Simulation results are presented that illustrate the convergence properties of the resulting system. Avenues for future work in this area are outlined