I Know What You're Doing: A Case Study on Case-Based Opponent Modeling and Low-Level Action Prediction

This paper focuses on an investigation of case-based opponent player modeling in the domain of simulated robotic soccer. While in previous and related work it has frequently been claimed that the prediction of low-level actions of an opponent agent in this application domain is infeasible, we show that – at least in certain settings – an online prediction of the opponent’s actions can be made with high accuracy. We also stress why the ability to know the opponent’s next low-level move can be of enormous utility to one’s own playing strategy.