Recognizing and predicting agent behavior with case based reasoning

Abstract

Case Based Reasoning is a feasible approach for recognizing and predicting behavior of agents within the RoboCup domain. Using the method described here, on average 98.4 percent of all situations within a game of virtual robotic soccer have been successfully classified as part of a behavior pattern. Based on the assumption that similar triggering situations lead to similar behavior patterns, a prediction accuracy of up to 0.54 was possible, compared to 0.17 corresponding to random guessing. Significant differences are visible between different teams, which is dependent on the strategic approaches of these teams.