MOLE: a knowledge acquisition tool that buries certainty factors

Abstract

MOLE is a knowledge acquisition tool for helping experts build systems that do differential diagnosis. Diagnostic expert systems often have to rely upon inferences that involve some degree of uncertainty. Typically, the tentativeness of the rules of inference is represented by certainty factors or some other cardinal measure of support. Unfortunately, this information is difficult to acquire from the experts. This paper describes how MOLE is able to dispense with certainty factors. By integrating into its problem-solving method several heuristic assumptions about how evidence relates to hypotheses, and by including in its knowledge acquisition process a way of generalizing the experťs preferences, MOLE does not need to elicit certainty factors from the domain experts or to internally represent the degree of support of an inference rule with certainty factors. This facilitates knowledge acquisition with no loss to diagnostic performance.