A cultural algorithm framework to evolve multiagent cooperation with evolutionary programming

Abstract

Cultural Algorithms have been previously used as a framework in which to evolve cooperative behavior within groups. Here they provide a framework within which to develop multiagent cooperation among a group of soccer players. The current system is used to learn several types of plays: offensive and defensive. In addition, plays can be learned without opposition, passive opposition, or active opposition. The Cultural Algorithms with Evolution Programming were effective learning all of these plays within several hundred generations each. In general, defensive plays were harder to learn than offensive ones. But, defensive plays with active protagonists were easier to learn than those with passive protagonists. This may be due to the fact that active protagonists provide additional information for the team members to use in formulating their plays. In addition, successful learning involved a coordination of individual adjustments among participating agents. A description of these adjustments in terms of the belief space for these agents is given.