Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Buchanan, B. G. and Shortliffe, E. H. (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, Mass.: Addison-Wesley.
Clancey, W. J. (1984). NEOMYCIN: reconfiguring a rule-based system with application to teaching. In Clancey, W. J. and Shortliffe, E. H., editors, Readings in Medical Artificial Intelligence, chapter 15, pages 361–381, Reading, Mass.: Addison-Wesley.
Clancey, W. J. (1985). Heuristic classification. Artificial Intelligence, 27:289–350.
DeJong, G. (1986). An approach to learning from observation. In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., editors, Machine Learning, Volume II, chapter 19, pages 571–590, Los Altos: Morgan Kaufmann.
Fu, L. and Buchanan, B. G. (1985). Inductive knowledge acquisition for rule based expert systems. Knowledge Systems Laboratory Report KSL-85-42, Stanford University, Stanford, CA.
Ginsberg, A., Weiss, S., and Politakis, P. (1985). SEEK2: a generalized approach to automatic knowledge base refinement. In Proceedings of the 1985 IJCAI, pages 367–374, Los Angeles, CA.
Michalski, R. S. (1983). A theory and methodology of inductive inference. In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., editors, Machine Learning: An Artificial Intelligence Approach, chapter 4, pages 83–134, Palo Alto: Tioga Press.
Mitchell, T., Utgoff, P. E., and Banerji, R. S. (1983). Learning by experimentation: acquiring and refining problem-solving heuristics. In Michalski, T. M., Carbonell, J. G., and Mitchell, T. M., editors, Machine Learning: An Artificial Intelligence Approach, pages 163–190, Palo Alto: Tioga Press.
Mitchell, T. M., Keller, R. M., and Kedar-Cabelli, S. T. (1986). Explanation-based generalization: a unifying view. Machine Learning, 1(1):47–80.
Mitchell, T. M., Mahadevan, S., and Steinberg, L. I. (1985). LEAP: a learning apprentice for VLSI design. In Proceedings of the 1985 IJCAI, pages 573–580, Los Angeles, CA.
Politakis, P. and Weiss, S. M. (1984). Using empirical analysis to refine expert system knowledge bases. Artificial Intelligence, 22(1):23–48.
Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., editors, Machine Learning, chapter 15, pages 463–482, Palo Alto: Tioga Press.
Smith, R. G., Winston, H. A., Mitchell, T. M., and Buchanan, B. G. (1985). Representation and use of explicit justifications for knowledge base refinement. In Proceedings of the 1985 IJCAI, pages 673–680, Los Angeles, CA.
Wilkins, D. C. (1987). Apprenticeship Learning Techniques For Knowledge Based Systems. PhD thesis, University of Michigan. Also, Knowledge Systems Lab Report KSL-88-14, Dept. of Computer Science, Stanford University, 1988, 153pp.
Wilkins, D. C. and Buchanan, B. G. (1986). On debugging rule sets when reasoning under uncertainty. In Proceedings of the 1986 National Conference on Artificial Intelligence, pages 448–454, Philadelphia, PA.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1989 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wilkins, D.C. (1989). Knowledge base refinement using apprenticeship learning techniques. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017226
Download citation
DOI: https://doi.org/10.1007/BFb0017226
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-50768-0
Online ISBN: 978-3-540-46081-7
eBook Packages: Springer Book Archive