Apprenticeship Learning Techniques for Knowledge Based Systems
Abstract
This thesis describes apprenticeship learning techniques for automation of the transfer of expertise. 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 explanation-based learning program constructs explanations of problem- solving actions in the domain of medical diagnosis. If no explanation is found, the incomplete domain theory (i.e., the medical knowledge base) is extended via the use of underlying domain theories and empirical methods so as to allow construction of an explanation. 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. When watching an expert, it improves a knowledge base for the pre-existing Heracles expert system shell. When watching a student apprentice, it models the student against the knowledge base and thereby identifies bugs and gaps in the student's fledgling expertise. 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, wherein a subset of the knowledge base can give better performance than the original knowledge base; incremental learning techniques are inappropriate when a knowledge base is sociopathic. Also, the synthetic agent method is presented; it provides a means of determining a performance upper bound for apprenticeship learning systems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1988
- Accession Number
- ADA225044
Entities
People
- David C. Wilkins
Organizations
- Stanford University