Automatic generation of behaviors for mobile robot by GA with automatically generated action rule-base

To design behaviors of a mobile robot for realizing given tasks, a designer has to make a set of rules which generate a proper action from a state of sensors. In general, however, it is difficult that the designer makes all set of rules since the number of rules is very large and the proper action for a state of sensors is not clear. Many methods have been proposed to solve such a problem using reinforcement learning genetic programming or genetic algorithm and so on. However, those methods are not sufficiently applied to the case in which the environment changes or is unknown. In this paper, new method which is efficiently applied to such a difficult case is proposed. The proposed method is constructed by genetic algorithm and action rule-base. The experimental results, using a mobile robot simulator, shows that the mobile robot can properly act in the unknown environments