Apprenticeship learning techniques for knowledge-based systems
This thesis describes apprenticeship learning techniques for automation of the transfer of expertise. Apprenticeship learning is a form of learning by watching, in which learning occurs as a byproduct of building explanations of human problem-solving actions. As apprenticeship is the most-powerful method that human experts use to refine and debug their expertise in knowledge-intensive domains such as medicine; this motivates giving such capabilities to an expert system. The major accomplishment in this thesis 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. The Odysseus learning program provides the first demonstration of using the same technique to transfer of expertise to and from an expert system knowledge base. Another major focus of this thesis is limitations of apprenticeship learning. It is shown that extant techniques for reasoning under uncertainty for expert systems lead to a sociopathic knowledge base.
- Research Organization:
- Michigan Univ., Ann Arbor (USA)
- OSTI ID:
- 6984116
- Country of Publication:
- United States
- Language:
- English
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