A Case-based Reasoning Approach to Imitating RoboCup Players

Abstract

We describe an effort to train a RoboCup soccer-playing agent playing in the Simulation League using case-based reasoning. The agent learns (builds a case base) by observing the behaviour of existing players and de-termining the spatial configuration of the objects the ex-isting players pay attention to. The agent can then use the case base to determine what actions it should per-form given similar spatial configurations. When observ-ing a simple goal-driven, rule-based, stateless agent, the trained player appears to imitate the behaviour of the original and experimental results confirm the observed behaviour. The process requires little human interven-tion and can be used to train agents exhibiting diverse behaviour in an automated manner.