Neural Networks in the Context of Autonomous Agents: Important Concepts Revisited

RALF SALOMON AI Lab, Computer Science Department, University of Zürich Winterthurerstrasse 190, 8057 Zurich, Switzerland FAX: +41-1-363 00 35; Email: [email protected] ABSTRACT : Artificial neural networks have been successfully used in many technical applications. They are also important tools for the control of autonomous agents. The major goal of research on autonomous agents is to study intelligence as the result of a system environment interaction, rather than understanding intelligence on a computational level. In contrast to other applications, autonomous agents might not distinguish between a learning and a performance phase; they have to continuously learn while they are behaving in their environment. Thus, a neural network for autonomous agents should feature incremental learning, should not exhibit overlearning and should not suffer from a high VC dimension. The review presented in this paper reveals that most existing models are not ideally suited for autonomous agents. The main goals of this paper are (1) to discuss the autonomous agents perspective, (2) to identify important properties of neural networks for autonomous agents, and (3), very important, to initiate new research on better suited network models.