Abstract
RoboCup is an interesting topic in investigating into autonomous agents, multi-agent collaboration, machine learning and so on. If you want to win the match, you should have good basic skills and coordination. In our team architecture, we use attractive force and repulsive force to choose movement direction for a player. Furthermore, we design team strategy and sub-strategies to be our playing strategies. We use different weighted formula to express strategies. In this thesis, we use a memory-based supervised learning strategy to select execution sub-strategy from passing the ball, shooting the ball, and dribbling sub-strategy. We concentrate on the angle and direction of the critical player who catches the ball in passing the ball sub-strategy and the opponents’ goal in shooting the ball sub-strategy. We use this learning strategy to solve the problem of transference of sub-strategies and make it transfer rationally and smoothly.