Construction Of State Space In Robocup

Machine Learning, the promising technique in AI disciple, had been widely applied to deal with problems in complex environment such as RoboCup, which is assumed as the ideal platform for research on AI and robotic. Nevertheless there are some very challengeous problems in completing the Machine Learning in such a complex environment. One of them is how to construct an appropriate state space, which should have two main features: to well describe the main characters of the environment states and to be small enough to be processed by ANN, RL, or other methods. In This paper, a new method to construct an appropriate state space In the complex environment is proposed, which fit into above two requirements. Author also has completed a sample state space to describe the middle field situation in RoboCup simulation game, which can be used to do the attach-route decision in RoboCup.