Mining Opponent Behavior: A Champion of RoboCup Coach Competition

Abstract

Opponent modeling is one of the most attractive and practical arenas in multi agent system (MAS) for predicting and identifying the future behaviors of opponent. This paper introduces an approach towards opponent modeling in RoboCup Soccer Coach Simulation. In this scene, an autonomous coach agent is able to identify the weaknesses or patterns of the opponent by analyzing the opponent's past games and advising own players. To gain this goal, we introduce a 3-tier learning architecture. At first, by gathering data from the environment, sequential events of the players are identified. Then the weaknesses or patterns of the opponent are predicted using statistical calculations. Eventually, by comparing the opponent patterns with the rest of team's behavior, a model of the opponent is constructed. According to this architecture, coach models the opponent and to simplify pattern recognition, provides an appropriate strategy to play against the opponent. This structure is tested in RoboCup Soccer Coach Simulation and MRLCoach was the champion at Iran Open 2006