Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-level skills, but they must also learn to work together and to adapt to the behaviors of different opponents. We are using a robotic soccer system to study these different types of multiagent learning: low-level skills, collaborative, and adversarial. Here we describe in detail our experimental framework. We present a learned, robust, low-level behavior that is necessitated by the multiagent nature of the domain, namely shooting a moving ball. We then discuss the issues that arise as we extend the learning scenario to require collaborative and adversarial learning.

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International Journal of Human-Computer Studies, Vol. 48, 1 (1998), pp. 83-104.

@Article{IJHCS, title={Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer}, author={Peter Stone and Manuela Veloso}, volume={48}, journal={International Journal of Human-Computer Studies}, number={1}, month={January}, pages={83-104}, url="http://www.cs.utexas.edu/users/ai-lab?IJHCS", year={1998} }