Applying dynamic networks and staged evolution for soccer robots

Behavior-based design has been widely recognized as one of the main approaches in traditional robot design. In this paper, we propose an integration of behavior-based approach and evolutionary algorithm, called an evolutionary behavior programming system. By adapting from the idea of Behavior Analysis, Behavioral Modules and Interactions are used for coding behavior-based control systems into a particular programming form. Therefore, the processes of Genetic Programming (GP) can be performed to evolve possible behavior-based control systems. Furthermore, with the intention to improve the learning performances in dynamic environments, a new idea of turning on/off each module in the network stochastically, called Dynamic Network, is applied. Experiments on soccer robots are simulated to verify this new approach. With the intention of training robots to play soccer games, staged evolution is used to speed up the learning process. Simulation experimental results demonstrate that this proposed method is highly promising.