Genetics-based machine learning and behavior-based robotics: a new synthesis

Intelligent robots should be able to use sensor information to learn how to behave in a changing environment. As environmental complexity grows, the learning task becomes more and more difficult. This problem is faced using an architecture based on learning classifier systems and on the structural properties of animal behavioral organization, as proposed by ethologists. After a description of the learning technique used and of the organizational structure proposed, experiments that show how behavior acquisition can be achieved are presented. The simulated robot learns to follow a light and to avoid hot dangerous objects. While these two simple behavioral patterns are independently learned, coordination is attained by means of a learning coordination mechanism. >