Knowledge Base Refinement Using Apprenticeship Learning Techniques

David C. Wilkins

This paper describes how apprenticeship learning techniques can be used to refine the knowledge base of an expert system for heuristic classification problems. The described method is an alternative to the long-standing practice of creating such knowledge bases via induction from examples. The form of apprenticeship learning discussed in this paper is a form of learning by watching, in which learning occurs by completing failed explanations of human problem-solving actions. An apprenticeship is the most powerful method that human experts use to refine their expertise in knowledge-intensive domains such as medicine; this motivates giving such capabilities to an expert system. A major accomplishment in this work is showing how an explicit representation of the strategy knowledge to solve a general problem class, such as diagnosis, can provide a basis for learning the knowledge that is specific to a particular domain, such as medicine.

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