Predictor: An Alternative Approach to Uncertain Inference in Expert Systems

Abstract

An alternative approach to uncertain inference in expert systems is described which might be regarded as a synthesis of techniques from automatic induction and mathematical statistics. It utilises a type of pattern matching in which comparisons are made between new cases (as yet unclassified) and a database of past cases (in which the outcome is known). The method uses a stepwise approach in which, at each stage, that evidence variable providing the greatest additional discriminating power between classes (above that already obtained) is utilised. It avoids relying on the assumption of conditional independence. A rudimentary system (PREDICTOR) which operates according to these principles has been written. Various adaptations to deal with missing and uncertain evidence are described, as are additional features such as a window, a facility for focusing discrimination on a subset of classes and a modification to deal with subjective data. I