SOME EXTENSIONS OF A SYSTEM FOR INFERENCE ON PARTIAL INFORMATION1

Some extensions of a system for inference on partial information, which uses production rules, are described. Basically, the system consists of RULES, an active set of rules (a subset of potentially large set of rules), partially ordered by specificity, and FACTS, a small active set of facts (a subset of potentially large set of data base facts). The critical feature of the inference method is that only a partial match of the antecedent of a rule is needed. Some selected extensions of the system are presented. These concern some approaches to the problems of selecting from an ambiguous response and, more importantly, transforming or dynamic clustering of FACTS and RULES. This latter problem is important because partial match is defined over the sets RULES and FACTS, and unless these sets are reasonably small, partial match can be an unmanageable operation. Several issues concerning the use of this inference system in certain applications are also briefly discussed.