Multimodule artificial neural network architectures for autonomous robot control through behavior modulation

In this paper we consider one of the big challenges when constructing modular behavior architectures for the control of real systems, that is, how to decide which module or combination of modules takes control of the actuators in order to implement the behavior the robot must perform when confronted with a perceptual situation. The problem is addressed from the perspective of - combinations of ANNs, each implementing a behavior, that interact through the modulation of their outputs. This approach is demonstrated using a three way predator-prey-food problem where the behavior of the individual should change depending on its energetic situation. The behavior architecture is incrementally evolved.