A multistrategy learning approach to domain modeling and knowledge acquisition

  • Gheorghe Tecuci
Part 1: Constructive Induction And Multi-Strategy Approaches
Part of the Lecture Notes in Computer Science book series (LNCS, volume 482)


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.


domain modeling knowledge acquisition multistrategy learning rule and concept learning 


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  1. 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.Google Scholar
  2. 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.Google Scholar
  3. Danyluk, A.P., The Use of Explanations for Similarity-Based Learning, Proceedings of IJCAI-87, pp. 274–276, Milan, Italy, 1987.Google Scholar
  4. 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.Google Scholar
  5. DeJong G., and Mooney R., Explanation-Based Learning: An Alternative View, in Machine Learning, vol.1, no. 2, pp. 145–176, 1986.Google Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. Kodratoff, Y., and Michalski, R.S. (eds), Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, vol.III, 1990.Google Scholar
  9. Lebowitz, M., Integrated Learning: Controlling Explanation, Cognitive Science, Vol. 10, No. 2, pp. 219–240, 1986.CrossRefGoogle Scholar
  10. 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.Google Scholar
  11. Michalski R.S., Theory and Methodology of Inductive Learning, Readings in Machine Learning, Dietterich T., and Shavlik J. (eds.) Morgan Kaufmann 1990.Google Scholar
  12. Michalski R. S., Toward a Unified Theory of Learning: Multistrategy Task-adaptive Learning, Submitted for publication in Machine Learning Journal, 1990.Google Scholar
  13. 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.Google Scholar
  14. Mitchell T.M., Version Spaces: An Approach to Concept Learning, Doctoral dissertation, Stanford University, 1978.Google Scholar
  15. 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.Google Scholar
  16. Morik K., Sloppy modeling, in Morik K. (ed), Knowledge Representation and Organization in Machine Learning, Springer Verlag, Berlin 1989.Google Scholar
  17. 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.Google Scholar
  18. Porter B., & Mooney R. (eds), Proceedings of the Seventh International Workshop on Machine Learning, Texas, Austin, 1990, Morgan Kaufman.Google Scholar
  19. Segre, A.M. (ed.), Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26–27, 1989.Google Scholar
  20. 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.Google Scholar
  21. Tecuci G., DISCIPLE: A Theory, Methodology, and System for Learning Expert Knowledge, Ph.D. Thesis, University of Paris-Sud, 1988.Google Scholar
  22. 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.Google Scholar
  23. 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.Google Scholar
  24. 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.Google Scholar
  25. Zhang, J. Learning Flexible Concepts from Examples: Employing the Ideas of Two-Tiered Concept Representation, PhD Thesis, University of Illinois at Urbana-Champaign, 1990.Google Scholar
  26. Wilkins, D.C., Clancey, W.J., and Buchanan, B.G., An Overview of the Odysseus Learning Apprentice, Kluwer Academic Press, New York, NY, 1986.Google Scholar
  27. Wrobel S., Demand-Driven Concept Formation, in Morik K.(ed), Knowledge Representation and Organization in Machine Learning, Springer Verlag, Berlin 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Gheorghe Tecuci
    • 1
  1. 1.Center for Artificial Intelligence, Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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