Learning through Decision Tree in Simulated Soccer Environment

Abstract

The robotic soccer is one of the most complex multiagent systems in which agents play the role of soccer players. The characteristics of such systems are: realtime, noisy, collaborative and adversarial. Therefore, playing agents must be capable to making decisions. This paper describes the use of decision tree to kick and catch the ball for two simulated soccer agents. One player shoots towards the goal and the other plays the role of goalkeeper. Experimental results have shown that rules achieved from decision tree lead to more effective operations in simulated soccer agent.