In a team-based multiagent system, the ability to construct a model of an opponent team's joint behavior can be useful for determining an agent's expected distribution over future world states, and thus can inform its planning of future actions. This paper presents an approach to team opponent modeling in the context of the RoboCup simulation coach competition. Specifically, it introduces an autonomous coach agent capable of analyzing past games of the current opponent, advising its own team how to play against this opponent, and identifying patterns or weaknesses on the part of the opponent. Our approach is fully implemented and tested within the RoboCup soccer server, and was the champion of the RoboCup 2005 simulation coach competition.

PDF, PS, HTML

In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1463-68, July 2006.

@InProceedings{AAAI06-coach, title={Know Thine Enemy: A Champion RoboCup Coach Agent}, author={Gregory Kuhlmann and William B. Knox and Peter Stone}, booktitle={Proceedings of the Twenty-First National Conference on Artificial Intelligence}, month={July}, pages={1463-68}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI06-coach", year={2006} }