Dynamic Adaptive Opponent Modeling: Predicting Opponent Motion while Playing Soccer

In dynamic multiagent domains with adversary agents, an agent has to adapt its behavior to the opponent actions in order to increase its ability to compete. A frequently used opponent modeling approach in these domains is to rely on an omniscient agent (e.g., a coach in a soccer environment) to classify the opponent and to communicate the opponent’s model (or a counter-strategy for that model) to other agents. In this paper, we propose an alternative opponent modeling approach where each agent observes and classifies online the adversaries it encounters into automatically learned models. Thus, our approach requires neither an omniscient agent nor pre-defined models. Empirical results obtained in a simulated robotic soccer environment promises a high suitability of this approach for real-time, dynamic, multiagent domains.