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The potential of machine learning techniques for expert systems

Published online by Cambridge University Press:  27 February 2009

Yoram Reich  and
Steven J. Fenves
Yoram Reich
Affiliation:
Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213, U.S.A.
Steven J. Fenves
Affiliation:
Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213, U.S.A.
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Abstract

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious. Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

Information

Type
Research Article
Information
AI EDAM , Volume 3 , Issue 3 , August 1989 , pp. 175 - 193
Copyright
Copyright © Cambridge University Press 1989

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References

Alspaugh, D. W. and Kunnoo, K. 1974. Optimum configurational and dimensional design of truss structures. Computers & Structures, 4, 755–770.CrossRefGoogle Scholar
Arciszewski, T., Mustafa, M. and Ziarko, W. 1987. A methodology of design knowledge acquisition for use in learning expert systems. International Journal of Man–Machine Studies, 27, 23–32.CrossRefGoogle Scholar
Buchanan, B. G. and Feigenbaum, E. A. 1978. Dendral and Meta-Dendral: their applications dimension. Artificial Intelligence, 11, 5–24.CrossRefGoogle Scholar
Carbonell, J. G. 1983. Learning by analogy: formulating and generalizing plans from past experience. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Press.Google Scholar
Carbonell, J. G. 1986. Derivational analogy. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: An Artifical Intelligence Approach. Volume II. Los Altos, CA: Morgan Kaufmann.Google Scholar
Davis, R. 1979. Interactive transfer of expertise: acquisition of new inference rules. Artificial Intelligence, 12, 121–157.CrossRefGoogle Scholar
de Kleer, J. and Brown, J. S. 1984. Qualitative physics based on confluences. Artificial Intelligence, 24, 7–83.CrossRefGoogle Scholar
DeJong, G. and Mooney, R. 1986. Explanation-based learning: an alternative view. Machine Learning, 1, 145–176.CrossRefGoogle Scholar
Dietterich, T. G. 1986. Learning at the knowledge level. Machine Learning, 1, 287–316.CrossRefGoogle Scholar
Dietterich, T. G. and Buchanan, B. G. 1983. The role of experimentation in theory formation. In: Michalski, R. S. (Ed.), Proceedings of the International Machine Learning Workshop. Monticello, III: The University of Illinois at Urbana-Champaign.Google Scholar
Doyle, R. J. 1986. Learning causal relations. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: A Guide to Current Research. Boston, MA: Kluwer.Google Scholar
Eshelman, L., Ehret, D., McDermott, J. and Tan, M. 1987. MOLE: a tenacious knowledge acquisition tool. International Journal of Man-Machine Studies, 26, 41–54.CrossRefGoogle Scholar
Falkenhainer, B. 1987. Scientific theory formation through analogical inference. In: Langley, P. (Ed.), Proceedings of The Fourth International Workshop on Machine Learning. Irvine, CA: Morgan Kaufmann.Google Scholar
Fisher, D. H. 1987. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172.CrossRefGoogle Scholar
Fisher, D. and Langley, P. 1985. Approaches to conceptual clustering. Proceeding of the Ninth International Joint Conference on Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann.Google Scholar
Forbus, K. 1984. Qualitative process theory. Artificial Intelligence, 24, 85–168.CrossRefGoogle Scholar
Ginsberg, A., Weiss, S. and Politakis, P. 1988. Automatic knowledge base refinement for classification systems. Artificial Intelligence, 35, 197–226.CrossRefGoogle Scholar
Huhns, M. N. and Acosta, R. D. 1987. Argo: An Analogical Reasoning System for Solving Design Problems (Tech. Rep. AI/CAD−902−97). Austin, TX: MCC Microelectronics and Computer Technology Corporation,Google Scholar
Iwasaki, Y. and Simon, H. A. 1986. Causality in device behavior. Artificial Intelligence, 29, 3–32.CrossRefGoogle Scholar
Kass, R. and Finin, T. 1987. Rules for the implicit acquisition of knowledge about the user. Proceedings of AAAI-87. Seattle, WA: Morgan Kaufmann.Google Scholar
Klinker, G., Bentolila, J., Genetet, S., Grimes, M. and McDermott, J. 1987. KNACK—report-driven knowledge acquisition. International Journal of Man-Machine Studies, 26, 65–79.CrossRefGoogle Scholar
Kolodner, J. L. 1987. Extending problem solver capabilities through case-based inference. In: P., Langley (Ed.), Proceedings of the Fourth International Workshop on Machine Learning. Irvine, CA: Morgan Kaufmann.Google Scholar
Kowalik, J. S. (Ed.). 1986. Coupling Symbolic and Numerical Computing in Expert Systems. Amsterdam: North Holland.Google Scholar
Kulikowski, C. A. 1983. Knowledge acquisition and learning in EXPERT. In: Michalski, R. S. (Ed.), Proceedings of the International Machine Learning Workshop. Monticello, ILL: The University of Illinois at Urbana-Champaign.Google Scholar
Kulkarni, D. and Simon, H. A. 1988. The processes of scientific discovery: the strategy of experimentation. Cognitive Science, 12, 139–175.CrossRefGoogle Scholar
Laird, J. and Newell, A. 1983. A Universal Weak Method (Tech. Rep. CMU-CS-83−141). Pittsburgh, PA: Computer Science Department, Carnegie-Mellon University,Google Scholar
Laird, J. E., Rosenbloom, P. S. and Newell, A. 1986. Chunking in Soar: the anatomy of a general learning mechanism. Machine Learning, 1, 11–44.CrossRefGoogle Scholar
Laird, J. E., Newell, A. and Rosenbloom, P. S. 1987. Soar: an architecture for general intelligence. Artificial Intelligence, 33, 1–64.CrossRefGoogle Scholar
Langley, P. 1985. Learning to search: from weak methods to domain-specific heuristics. Cognitive Science, 9, 217–260.Google Scholar
Langley, P. and Simon, H. A. 1981. The central role of learning in cognition. In: Anderson, J. R. (Ed.), Cognitive Skills and Their Acquisition. Hillsdale, NJ: Erlbaum.Google Scholar
Langley, P. and Carbonell, J. 1984. Approaches to machine learning. Journal of the American Society for Information Science, 35, 306–316.CrossRefGoogle Scholar
Langley, P. and Nordhausen, B. 1986. A framework for empirical discovery. (Tech. Rep. 86−19). Irvine, CA: Information and Computer Science Department, University of California at Irvine.Google Scholar
Langley, P., Zytkow, J., Simon, H. A. and Bradshaw, G. L. 1986. The search for regularity: four aspects of scientific discovery. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach Volume II. Los Altos, CA: Morgan Kaufmann.Google Scholar
Lenat, D. B. 1983. EURISKO: a program that learns new heuristics and domain concepts: the nature of heuristics III: program design and results. Artificial Intelligence, 21, 61–98.CrossRefGoogle Scholar
Lenat, D. B. and Brown, J. S. 1984. Why AM and Eurisco appear to work. Artificial Intelligence, 23, 269–294.CrossRefGoogle Scholar
Lenat, D., Prakash, M. and Shepherd, M. 1986. CYC: using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine, 6, 65–85.Google Scholar
Marcus, S., McDermott, J. and Wang, T. 1985. Knowledge acquisition for constructive systems. Proceedings of The Ninth International Joint Conference on Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann.Google Scholar
Marcus, S., Stout, J. and McDermott, J. 1986. VT: an expert elevator configurer (Tech. Rep. CMU-CS-86−169). Pittsburgh, PA: Computer Science Department, Carnegie-Mellon University.Google Scholar
McDermott, J. 1986. Making expert systems explicit. In: Kugler, H. J. (Ed.), Information Processing 86. Amsterdam: North-Holland.Google Scholar
Michalski, R. S. 1983. Understanding the nature of learning: issues and research directions. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Press.CrossRefGoogle Scholar
Michalski, R. S. and Chilausky, R. L. 1980. Knowledge acquisition by encoding expert rules versus computer induction from examples: a case study involving soybean pathology. International Journal of Man-Machine Studies, 12, 63–87.CrossRefGoogle Scholar
Michalski, R. S. and Stepp, R. 1983. Learning from observation: conceptual clustering. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.) Machine Learning: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Press.CrossRefGoogle Scholar
Michell, A. G. M. 1904. The limits of economy of material in frame-structures. Philosophical Magazine, Series 6, 8(47), 589–595.CrossRefGoogle Scholar
Mitchell, T. M. 1982. Generalization as search. Artificial Intelligence, 18, 206–226.CrossRefGoogle Scholar
Mitchell, T. M. 1984. Toward combining empirical and analytical methods for inferring heuristics. In: A., Elithorn and R., Banerji (Eds.) Artificial and Human Intelligence. Amsterdam: North–Holland.Google Scholar
Michell, T. M., Utgoff, P. E. and Banerji, R. 1983. Learning by experimentation: acquiring and refining problem-solving heuristics. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learing: An Artificial Intelligence Approach. Palo Alto, CA: Tioga Press.Google Scholar
Mitchell, T., Mahadevan, S. and Steinberg, L. 1985 a. LEAP: a learning apprentice for VLSI design. Proceedings of The Ninth International Joint Conference on Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann.Google Scholar
Mitchell, T. M., Steinberg, L. I. and Shulman, J. S. 1985 b. A knowledge-based approach to design. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(5), 502–510.CrossRefGoogle ScholarPubMed
Mitchell, T. M., Keller, R. M. and Kedar-Cabelli, S. T. 1986. Explanation-based generalization: a unifying view. Machine Learning, 1, 47–86.CrossRefGoogle Scholar
Mostow, J. 1986. Why are design derivations hard to replay. In: Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eds.), Machine Learning: A Guide to Current Research. Boston, MA: Kluwer.Google Scholar
Newell, A. 1982. The knowledge level. Artificial Intelligence, 18, 87–127.CrossRefGoogle Scholar
Owen, J. B. B. 1965. The Analysis and Design of Light Structures. New York: American Elsevier.Google Scholar
Quinlan, J. R. 1986. Induction of decision trees. Machine Learning, 1, 81–106.CrossRefGoogle Scholar
Quinlan, J. R. 1988. Induction, knowledge and expert systems. In: Gero, J. S. and R., Stanton (Eds.), Artificial Intelligence Developments and Applications. Amsterdam: North-Holland.Google Scholar
Rosenbloom, P. S., Laird, J. E. 1986. Mapping explanation-based generalization onto Soar. Proceedings of AAAI-86. Philadelphia, PA: Morgan Kaufmann.Google Scholar
Rosenbloom, P. S., Laird, J. E., McDermott, J., Newell, A., Orciuch, E. 1985. Rl-Soar: an experiment in knowledge-intensive programming in a problem-solving architecutre. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(5), 561–569.CrossRefGoogle Scholar
Save, M. and Prager, W. (Eds.). 1985. Structural Optimization—Volume 1: Optimality Criteria. New York: Plenum Press.CrossRefGoogle Scholar
Simon, H. A. and Lea, G. 1974. Problem solving and rule induction: A unified view. In: Gregg, L. W. (Ed.), Knowledge and Cognition. Potomac, MD: Lawrence Erlbaum.Google Scholar
Spillers, W. R. 1975. Iterative Structural Design. Amsterdam: North-Holland.Google Scholar
Steier, D. M., Laird, J. E., Newell, P. S., Rosenbloom, P. S., Flynn, R., Golding, A., Folk, T. A., Shivers, O. G., Unruh, A. and Yost, G. R. 1987. Varieties of learning in Soar: 1987. In: Langley, P. (Ed.), Proceedings of the Fourth International Workshop on Machine Learning. Irvine, CA: Morgan Kaufmann.Google Scholar
Weiss, S. and Kulikowski, C. 1979. EXPERT: a system for developing consultation models. Proceedings of The Sixth International Joint Conference on Artificial Intelligence. Tokyo: Morgan Kaufmann.Google Scholar
Winston, P. 1975. Learning structural descriptions from examples. In: P., Winston (Ed.), The Psychology of Computer Vision. New York: McGraw-Hill.Google Scholar