Learning reactive and planning rules in a motivationally autonomous animat

Abstract

This work describes a control architecture based on a hierarchical classifier system. This system, which learns both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it performs according to its perception of the external environment, to its physiological or internal state, to the consequences of its current behavior, and to the expected consequences of its future behavior. The adaptive faculties of this architecture are illustrated within the context of a navigation task, through various experiments with a simulated and a real robot.