Learning coordination strategies for cooperative multiagent systems

A central issue in the design of cooperative multiagent systems is how to c oordinate the behavior of the agents to meet the goals of the designer. Tra ditionally, this had been accomplished by hand-coding the coordination stra tegies. However, this task is complex due to the interactions that can take place among agents. Recent work in the area has focused on how strategies can be learned. Yet, many of these systems suffer from convergence, complex ity and performance problems. This paper presents a new approach for learni ng multiagent coordination strategies that addresses these issues. The effe ctiveness of the technique is demonstrated using a synthetic domain and the predator and prey pursuit problem.