Behavior Acquisition in RoboCup Middle Size League Domain

Abstract

The RoboCup middle size league is one of the leagues that have the longest histories in RoboCup. This league has unique features, for example, bigger robots (around 45cm square) plays on the largest field (say, 18m×12m in 2007), any global sensory system is not allowed to use, all robots have on-board vision systems and controllers. Each robot plays based on its own sensory information, and it can share some information with teammates and a coach box located outside the playing field over wireless communication, then, shows some cooperative behaviors among them during the game. This chapter briefly introduces research activities in RoboCup middle size league. A variety of research topics have been attacked in this league. Some of them are common to other real robot leagues such as small size and 4-legged leagues. For example, robust real-time onboard vision system, precise localization based on vision system, and design of cooperative behavior are actively investigated in RoboCup middle size league. On the other hand, skill and cooperative/competitive behavior acquisition/emergence based on machine learning techniques is also well-studied. The latter is focused on in this chapter. First, a purposive behavior acquisition of a single robot based on machine learning technique is introduced. Reinforcement learning is one of machine learning techniques and extensively studied to be applied to acquisition of robot behavior like shooting a ball into a goal. It has a simple framework and algorithm to be applied to robots however some difficulties exist in practical use because of its simplicity. In order to overcome these problems, some modular learning and hierarchical systems have been proposed. Not only reinforcement learning but also evolutional methods have been investigated as well. Some examples will be shown. Next, studies on cooperative/competitive behavior acquisition based on machine learning techniques are introduced. Application of machine learning to multi-agent system usually has some difficulties because of complex dynamics of the system. The complexity is induced by decision making by multi-players, growing amount of information to decide an action by an individual, perceptual aliasing, and so on. In order to reduce the complexity, wireless communication between teammates is commonly used. In case of unavailability of communication between players, for example, lack of communication with opponents,