BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-86-1116
       ENTRY:: May 01, 1995
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Inductive knowledge acquisition for rule-based expert systems
        TYPE:: Technical Report
      AUTHOR:: Fu, Li-Min
      AUTHOR:: Buchanan, Bruce G.
        DATE:: October 1985
       PAGES:: 42
    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.
       NOTES:: [Adminitrivia V1/Prg/19950501]
         END:: STAN//CS-TR-86-1116