Uncertainty computations in neural networks

Abstract

In a three-valued neural logic network, the strengths of the nodes are confined to the ordered pairs (1,0), (0,1) and (0,0). The first two pairs represent TRUE and FALSE respectively. The meaning of the third pair depends on the type of logic used. In Kleene's logic, (0,0) represents UNKNOWN. In Bochvar's logic, it represents MEANINGLESS. In this paper we introduced neural networks that allowed the strengths to be an ordered pair of real numbers the sum of which does not exceed one. Uncertainty is expressed by having a sum of less than one. This allows us to treat uncertainties in facts, rules as well as logical operations in a unifying way.