Learning fault diagnosis heuristics from device descriptions

This chapter describes a technique for the construction of the knowledge base of a diagnostic expert system. Diagnosis heuristics (i.e., efficient rules that encode empirical associations between atypical device behavior and device failures) are learned from information implicit in device models. This approach is desirable since less effort is required to obtain information about device functionality and connectivity to define device models than to encode and debug diagnosis heuristics provided by a domain expert. This approach to learning integrates failure-driven learning and explanation-based learning.