Behavior Acquisition and Classification: A Case Study in Robotic Soccer

Abstract

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 adversar-ial agents inferring with the accomplishment of goals. Once identified, the agents can then respond to recent strategies and adapt to improve performance. This research works under the hypothesis that fast and useful adaptation can be done by analogy to previous ob-servations. We introduce methods to extract similarities in temporal observations of the world. First, past observations are organized into a set of behavior classes. By analyzing similarities, the current adversary can be classified into this set of behavior classes. The agents can then employ the most effective strategy against that behavior group. The test domain for this research is the Soccer Server Sys-