Evidential support logic programming

An expert system is a computer program which can act in a similar way to a human expert in a restricted domain of application from the point of view of solving problems, taking decisions, planning and giving advice. It consists of two parts. One part is a knowledge base consisting of that knowledge used by the expert in his performance. A second part is an inference engine which allows queries to be answered by asking questions of the environment and performing evidential reasoning.This paper is concerned with the knowledge representation and inference mechanism for evidential reasoning. Man's knowledge consists of statements which cannot be guaranteed to be true and is expressed in a language containing imprecise terms. Uncertainties, either of a probabilistic or fuzzy nature, cannot be ignored when modelling human expertise. Not all practical reasoning takes the form of deductive inference. For practical affairs we use inductive, abductive, analogical and plausible reasoning methods and for each of these the concept of the strength of evidence would seem to be important.We describe a support logic programming system which generalises logic programming to the case in which various forms of uncertainty can be included. In this system a conclusion does not logically follow from some axioms but is supported to a certain degree by means of evidence. The negation of the conclusion is also supported to a certain degree and the two supports do not necessarily add up to one.A calculus for such a support logic programming system is described and applications to its use in expert systems and its use in providing recursive definitions of fuzzy concepts are given.