Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning

Michael Pazzani

This paper discusses an application of failure-driven learning to the construction of the knowledge base of a diagnostic expert system. Diagnosis heuristics (i.e., efficient rules which encode empirical associations between atypical device behavior and device failures) are learned from information implicit in device models. This approach is desireable since less effort is required to obtain information about device functionality and connectivity to define device models than to encode and debug diagnosis heuristics from a domain expert. We give results of applying this technique in an expert system for the diagnosis of failures in the attitude control system of the DSCS-III satellite. The system is fully implemented in a combination of LISP and PROLOG on a Symbolics 3600. The results indicate that realistic applications can be built using this approach. The performance of the diagnostic expert system after learning is equivalent to and, in some cases, better than the performace of the expert system with rules supplied by a domain expert.


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