Synthesizing intelligent behavior: A learning paradigm

Abstract

This paper presents a self-improving reactive control system for autonomous agents. It relies on the emergence of more global behavior from the interaction of smaller behavioral units. To simplify and automate the design process techniques of learning and adaptivity are introduced at three stages: first, improving the robustness of the system in order to deal with noisy, inaccurate, or inconsistent sensor data, second, improving the performance of the agent in the context of different goals (behaviors), and third, extending the capabilities of the agent by coordinating behaviors it is already able to deal with to solve more general and complex tasks.