Parallel Formulation of Evidential-Reasoning Theories

Abstract

There it no general consensus on how bett to attack evidential-reasoning (ER) problems, particularly in expert-system applications. Several approaches have evolved, but they have their roots in diverse fields, such as statistics and philosophy, and have neither a common terminology nor a common set of assumptions. The research reported here provides two useful results. First, it structures the evidential-reasoning problem in a general paradigm robust enough to be of practical use in design and construction of expert systems. Second, it uses this paradigm to formulate five important theoretical approaches in a parallel fashion in order to identify key assumptions, similarities, and differences. The five approaches discussed are classical Bayes, convex Bayes, Dempster-Shafer, Kyburg, and possibility.