Judgmental Reasoning for Expert Systems

We present a new system for plausible reasoning in expert systems. It is an extension of the Horn clause subset of first order logic, and is distinguished by its use of non-numeric certainties and the assignment of certainties to logical formulae rather than events. Model theoretic and ftxpoint semantics are sketched for this language. An implementation in the logic programming language Prolog is described and the advantages of the system are discussed. 1. Introduction Some kind of judgemental or probabilistic reasoning forms an essential part of many expert systems. The need for such reasoning arises whenever knowledge is incomplete or data uncertain. This is the case in many of the problem domains to which expert systems are applied. In this paper we present a system for judge-mental reasoning which differs in two respects from the more usual approaches. It uses a non-numeric representation for belief, and assigns certainties to logical formu-lae rather than events. It is an extension to the work described in (Shapiro 1983).