Integration of Multiple Neural Networks Evolved on
Cellular Automata by Action Selection Mechanism
(2001?)Abstract
There has been extensive research of developing the
controller for a mobile robot. Especially, several
researchers have constructed the mobile robot
controller that can avoid obstacles, evade predators,
or catch moving prey by evolutionary algorithms such as
genetic algorithm and genetic programming. 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. In this paper, we have attempted to solve
this problem by combining several modules evolved to do
a simple behavior by Maes's Action Selection Mechanism.
Experimental results show that this approach has
potential to develop a sophisticated neural controller
for complex environments.