C4.5: programs for machine learning
C4.5: programs for machine learning
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Recognizing Probabilistic Opponent Movement Models
RoboCup 2001: Robot Soccer World Cup V
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Policy recognition for multi-player tactical scenarios
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Generating Dynamic Formation Strategies Based on Human Experience and Game Conditions
RoboCup 2007: Robot Soccer World Cup XI
On the Usefulness of Interactive Computer Game Logs for Agent Modelling
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
On-the-fly generation of multi-robot team formation strategies based on game conditions
Expert Systems with Applications: An International Journal
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The winning advantage: using opponent models in robot soccer
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Identifying and utilizing subgroup coordination patterns in team adversarial games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Strategy patterns prediction model (SPPM)
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Action selection via learning behavior patterns in multi-robot domains
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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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.