Uncertainty in neural networks

Abstract

Uncertainty in AI applications, as they apply to inductive inference, is often dealt with by modeling heuristic methods of inference based on different kinds of logic, binary, multivalued or fuzzy, simulated on digital computers with probability, possibility or belief theories. The authors suggest that uncertainty may be managed naturally, and even used profitably, in cooperative, self-organizing, dynamical physical systems, and in neural networks. New classes of powerful cooperative computation and learning (C&L) machines are possible, with the class of artificial neural networks being just an early, rather rudimentary, example. The temporal behavior of classical dynamical physical systems was investigated for C&L models. The case of deterministic chaos is considered in studying C&L properties in dynamical system behavior. Deterministic chaos underlines the behavior of a class of physical systems of special interest, whose unpredictability is derived from sensitive dependence on initial conditions in a sustained way which exaggerates uncertainty. Non-Lipschitzian unpredictability is considered. >