Coordination of multiple behavior modules evolved on CAM-Brain

In behavior-based robotics the control of a robot is shared between a set of purposive perception-action units, called behaviors. A major issue in the design of behavior-based control systems is the formulation of effective mechanisms for coordination of the behaviors' activities into strategies for rational and coherent behavior. Early, there has been extensive work to construct an optimal controller for a mobile robot by evolutionary approaches such as genetic algorithm, genetic programming, and so on. In this line of research, we have also presented a method of applying CAM-Brain, evolved neural networks based on cellular automata (CA), to control a mobile robot. However, this approach has a limitation to make the robot to perform appropriate behavior in complex environments. The multi module coordination method can make complex and general behaviors by combining several modules evolved or programmed to do a simple behavior. In this paper, we have attempted to coordinate several modules evolved to do a simple behavior by Maes's Action Selection Mechanism. Maes has proposed a mechanism for action selection, which is reviewed here and is evaulated using a simulated environment. Experimental results show that this approach has potential to develop a sophisticated evolutionary neural controller for complex environments.