Genetics-Based Machine Learning and Behaviour-Based Robotics: A New Synthesis

摘要

Intelligent robots should be able to use sensor information to learn how tobehave in a changing environment. As environmental complexity grows, the learning task becomes more and more difficult. The authors face the problem 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, they present experiments that show how behavior acquisition can be achieved. Their simulated robot learns to follow a light and to avoid hot dangerous objects. While these two simple behaviors are independently learned, coordination is attained by means of a learning coordination mechanism. Again this capacity is demonstrated by performing a number of experiments. (Copyright (c) GMD 1991.)