Opponent Provocation and Behavior Classification: A Machine Learning Approach

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 a novel approach using rule based expert system towards opponent modeling in RoboCup Soccer Coach Simulation. In this scene, an autonomous coach agent is able to identify the patterns of the opponent by analyzing the opponent's past games and advising own players. For this purpose, the main goal of our research comprises two complementary parts: (a) developing a 3-tier learning architecture for classifying opponent behaviors. To achieve this objective, sequential events of the game are identified using environmental data. Then the 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. (b) designing a rule based expert system containing provocation strategies to expedite detection of opponent patterns. These items mentioned are used by coach, to model the opponent and generate an appropriate strategy to play against the opponent. This structure is tested in RoboCup Soccer Coach Simulation and MRLCoach was the champion at RoboCup 2006 in Germany.