Report Number: CS-TR-86-1116
Institution: Stanford University, Department of Computer Science
Title: Inductive knowledge acquisition for rule-based expert systems
Author: Fu, Li-Min
Author: Buchanan, Bruce G.
Date: October 1985
Abstract: The RL program was developed to construct knowledge bases automatically in rule-based expert systems, primarily in MYCIN-like evidence-gathering systems where there is uncertainty about data as well as the strength of inference, and where rules are chained together or combined to infer complex hypotheses. This program comprises three subprograms: (1) a program that learns confirming rules, which employs a heuristic search commencing with the most general hypothesis; (2) a subprogram that learns rules containing intermediate concepts, which exploits the old partial knowledge or defines new intermediate concepts, based on heuristics; (3) a program that learns disconfirming rules, which is based on the expert's heuristics to formulate disconfirming rules. RL's validity has been demonstrated with a performance program that diagnoses the causes of jaundice.