Game Player Strategy Pattern Recognition by Using Radial Basis Function

Pattern recognition has been successfully used in different application areas, its application on identifying playerpsilas strategy during the gameplay which is called player strategy pattern recognition (PSPR), is another interesting area. PSPR can greatly improve game AIpsilas adaptability, and as a result the entertainment of game is promoted. In this paper, pac-man game is used as a test-bed. Kernel classifier of radial basis function (RBF) is chosen to analyze off-line data from gamers who are choosing different strategies, in other words the classifiers are trained with sample data from players using different strategies. The method attempts to use the trained classifier to predict strategy pattern of a future player based on the data captured from its gameplay. This paper presents the basic principle of the PSPR by using the RBF theoretic approach and discusses the results of the experiments.