Abstract
The use of artificial intelligence technique to access, analyze, and integrate different types of knowledge under a single diagnostic concept is described. Repair statistics and field experience are handled by an empirical knowledge (shallow reasoning) diagnostic system in order to retain the experience of expert test personnel. Computer-aided-design knowledge is handled by model-based (deep reasoning) diagnostic systems in order to extract diagnostics directly from design data. Combining these approaches overcomes limitations of the individual techniques and provides a more powerful diagnostic system. The Westinghouse expert diagnostic system is considered as an example.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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