RoboCup is a particularly good domain for studying multi-agent systems. A wide variety of MAS issues can be studied in robotic soccer, in which the theory, algorithm and architecture of agent system can be evaluated. Because of the inherent complexity of MAS, there are many interests in using machine learning techniques to handle it. This paper investigates and discusses the machine-learning techniques used in RoboCup. The background is firstly presented and the application of machine learning in RoboCup is lately demonstrated with some top simulation teams. The machine-learning system in NDSocTeam is also introduced. Finally some open issues in this field are pointed out.
