Behavior Acquisition and Classification: A Case Study in Robotic Soccer

Patrick Riley and Manuela Veloso, Carnegie Mellon University

Increasingly in domains with multiple intelligent agents, each agent must be able to identify what the other agents are doing. This is especially important when there are adversarial agents inferring with the accomplishment of goals. Once identified, the agents can then respond to recent strategies and adapt to improve performance. We present an approach to doing adaptation which relies on classification of the current adversary into predefined adversary classes. For feature extraction, we present a windowing technique to abstract useful but not overly complicated features. The feature extraction and classification steps are fully implemented in the domain of simulated robotic soccer, and experimental results are presented.


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