Abstract
This paper presents an approach to domain modeling and knowledge acquisition that consists of a gradual and goal-driven improvement of an incomplete domain model provided by a human expert. Our approach is based on a multistrategy learning method that allows a system with incomplete knowledge to learn general inference or problem solving rules from specific facts or problem solving episodes received from the human expert. The system will learn the general knowledge pieces by considering all their possible instances in the current domain model, trying to learn complete and consistent descriptions. Because of the incompleteness of the domain model the learned rules will have exceptions that are eliminated by refining the definitions of the existing concepts or by defining new concepts.
On leave from Research Institute for Informatics, Bucharest, Romania
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Bhatnagar, R.K., and Kanal L.N. (1986) Handling Uncertain Information: A Review of Numeric and Non-numeric Methods, in Kanal L.N. and Lemmer J.F. (eds) Uncertainty in Artificial Intelligence, Elsevier Science Publishers, North-Holland, 3–26.
Boose, J.H., Gaines, B.R., and Ganascia, J.G.(eds), Proceedings of the Third European Workshop on Knowledge Acquisition for Knowledge-based Systems, Paris, July, 1989.
Danyluk, A.P., The Use of Explanations for Similarity-Based Learning, Proceedings of IJCAI-87, pp. 274–276, Milan, Italy, 1987.
Dietterich, T.G., and Flann, N.S., An Inductive Approach to Solving the Imperfect Theory Problem, Proceedings of 1988 Symposium on Explanation-Based Learning, pp. 42–46, Stanford University, 1988.
DeJong G., and Mooney R., Explanation-Based Learning: An Alternative View, in Machine Learning, vol.1, no. 2, pp. 145–176, 1986.
Kodratoff Y., and Ganascia J-G., Improving the Generalization Step in Learning, in Michalski R., Carbonell J. & Mitchell T. (eds) Machine Learning: An Aritificial Intelligence Approach, Vol. 2, Morgan Kaufmann 1986, pp. 215–244.
Kodratoff Y., and Tecuci G., Techniques of Design and DISCIPLE Learning Apprentice, International Journal of Expert Systems: Research and Applications, vol.1, no.1, pp. 39–66, 1987.
Kodratoff, Y., and Michalski, R.S. (eds), Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, vol.III, 1990.
Lebowitz, M., Integrated Learning: Controlling Explanation, Cognitive Science, Vol. 10, No. 2, pp. 219–240, 1986.
Michalski, R.S., Carbonell J.G., and Mitchell T.M. (eds), Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, vol.I, 1983, vol.II, 1986.
Michalski R.S., Theory and Methodology of Inductive Learning, Readings in Machine Learning, Dietterich T., and Shavlik J. (eds.) Morgan Kaufmann 1990.
Michalski R. S., Toward a Unified Theory of Learning: Multistrategy Task-adaptive Learning, Submitted for publication in Machine Learning Journal, 1990.
Minton, S., Carbonell, J.G., Etzioni, O., Knoblock C., Kuokka D.R., Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System, Proceedings of the 4th International Machine Learning Workshop, pp. 122–133, University of California, Irvine, 1987.
Mitchell T.M., Version Spaces: An Approach to Concept Learning, Doctoral dissertation, Stanford University, 1978.
Mitchell T.M., Keller R.M., and Kedar-Cabelli S.T., Explanation-Based Generalization: A Unifying View, Machine Learning, vol.1, no.1, pp. 47–80, 1986.
Morik K., Sloppy modeling, in Morik K. (ed), Knowledge Representation and Organization in Machine Learning, Springer Verlag, Berlin 1989.
Pazzani M.J., Integrating Explanation-based and Empirical Learning Methods in OCCAM, in Sleeman D. (ed), Proceedings of the Third European Working Session on Learning, Glasgow, 1988.
Porter B., & Mooney R. (eds), Proceedings of the Seventh International Workshop on Machine Learning, Texas, Austin, 1990, Morgan Kaufman.
Segre, A.M. (ed.), Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26–27, 1989.
Tecuci G., Kodratoff Y., Bodnaru Z., and Brunet T., DISCIPLE: An expert and learning system, Expert Systems 87, Brighton, December, 14–17, in D. S. Moralee (ed): Research and Development in Expert Systems IV, Cambridge University Press, 1987.
Tecuci G., DISCIPLE: A Theory, Methodology, and System for Learning Expert Knowledge, Ph.D. Thesis, University of Paris-Sud, 1988.
Tecuci, G. and Kodratoff Y., Apprenticeship Learning in Imperfect Theory Domains, in Kodratoff Y., and Michalski R.S. (eds), Machine Learning: An Artificial Intelligence Approach, vol. III, Morgan Kaufmann, 1990.
Tecuci, G. and Michalski R., A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justification, to appear in Reports of Machine Learning and Inference Laboratory, George Mason University, 1991.
van Melle, W., Scott, A.C., Bennett, J.S., and Peairs, M., The EMYCIN Manual, Report no. HPP-81-16, Computer Science Department, Stanford University, 1981.
Zhang, J. Learning Flexible Concepts from Examples: Employing the Ideas of Two-Tiered Concept Representation, PhD Thesis, University of Illinois at Urbana-Champaign, 1990.
Wilkins, D.C., Clancey, W.J., and Buchanan, B.G., An Overview of the Odysseus Learning Apprentice, Kluwer Academic Press, New York, NY, 1986.
Wrobel S., Demand-Driven Concept Formation, in Morik K.(ed), Knowledge Representation and Organization in Machine Learning, Springer Verlag, Berlin 1989.
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Tecuci, G. (1991). A multistrategy learning approach to domain modeling and knowledge acquisition. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017001
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DOI: https://doi.org/10.1007/BFb0017001
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