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The Role of Machine Learning in Knowledge Acquisition

  • Kai Zercher
  • Bernd Radig
Conference paper

Abstract

Acquiring the knowledge for a knowledge-based system has proven to be a difficult task. Machine learning techniques are one possible approach to tackle this problem. Three case studies are described which show that machine learning techniques can produce superior results than more traditional knowledge acquisition techniques. Finally, some conclusion drawn from these examples are presented.

Keywords

Knowledge Acquisition Machine Learning Technique Domain Theory Inductive Learning Repertory Grid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bergadano, F., Giordana, A., “A knowledge intensive approach to concept induction”, p. 305 – 317, proceedings of the fifth international conference on machine learning, Ann Arbor, 1988.Google Scholar
  2. 2.
    Blaffert, T., “Ein Gesamtsystem zur Auswertung von Infrarotspektren durch Lernen und Erkennen spektraler Merkmale sowie Zerlegung und Synthese chemischer Strukturgraphen”, Dissertation (in preparation), Universität Hamburg, 1989.Google Scholar
  3. 3.
    Boose, J.H., Gaines, B.R., “Knowledge acquisition for knowledge-based systems”, AAAI 88, tutorial notes MP4, 1988.Google Scholar
  4. 4.
    Boose, J.H., “A survey of knowledge acquisition techniques and tools”, Knowledge Acquisition, Vol. 1 Nr. 1, p. 3 – 37, 1989.Google Scholar
  5. 5.
    Buchanan, B.G. et al., “Models of learning systems”, in “Encyclopedia of computer science and technology”, p. 24 – 50, Marcel Dekker, New York, 1978.Google Scholar
  6. 6.
    Carboneil, J.G., “Learning by analogy: Formulating and generalizing plans from past experience”, Chap. 5 [12].Google Scholar
  7. 7.
    Carbonell, J.G., “Derivational analogy: A theory of reconstructive problem solving and expertise acquisition”, Chap. 14 [13].Google Scholar
  8. 8.
    Clancey, W., presentation at the first knowledge acquisition for knowledge-based systems workshop, Banff, Canada, 1986.Google Scholar
  9. 9.
    Cohen, P.R., Feigenbaum, E.A. (eds.), ”The handbook of artificial intelligence”, Vol. 3, chap. 14, William Kaufmann, 1982.Google Scholar
  10. 10.
    DeJong, G., Mooney, R., “Explanation-based learning: An alternative view”, Machine Learning 1, p. 145 – 176, 1986.Google Scholar
  11. 11.
    Ellman, T., “Explanation-based learning: A survey of programs and perspectives”, ACM computing surveys, Vol. 21, Nr. 2, p. 163 – 221, June 1989.Google Scholar
  12. 12.
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.), “Machine learning: An artificial intelligence approach”, Vol. 1, Morgan Kaufmann, 1983 (also Springer Verlag, 1984).Google Scholar
  13. 13.
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.), “Machine learning: An artificial intelligence approach”, Vol. 2, Morgan Kaufmann, 1986.Google Scholar
  14. 14.
    Michalski, R.S., Chilausky, R.L., “Learning by being told and learning from examples: An experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis.”, International Journal of Policy Analysis and Information Science, Vol. 4, No. 2, p. 125–161,1980.Google Scholar
  15. 15.
    Mitchell, T.M., Keller, R.M., Kedar-Cabelli, S.T., “Explanation-based learning: A unifying view”, Machine Learning 1, p. 47 – 80, 1986.Google Scholar
  16. 16.
    Pazzani, M.J., “Integrating explanation-based and empirical learning methods in OCCAM”, proceedings of the third European working session on learning (EWSL), 1988.Google Scholar
  17. 17.
    Pearce, D.A., “The induction of fault diagnosis systems from qualitative models”, p. 353–357, proceedings AAAI 88, 1988.Google Scholar
  18. 18.
    Shaw, M.L.G., Gaines, B.R., “An interactive knowledge-elicitation technique using personal construct technology”, in Kidd, A. (ed.), “Knowledge acquisition for expert systems. A practical handbook”, Plenum Press, 1987Google Scholar
  19. 19.
    Shaw, M.L., Gaines, B. R., “Knowledge acquisition: Some foundations, manual methods and future trends”, in Boose, J., et. al. (eds.), “Proceedings of the third European workshop on knowledge acquisition for knowledge-based systems” (EKAW 89), 1989.Google Scholar
  20. 20.
    Simon, H.A., “Why should machines learn?”, Chap. 2, [12].Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Kai Zercher
    • 1
    • 2
  • Bernd Radig
    • 1
  1. 1.Institut für InformatikTU MünchenMünchen 80Germany
  2. 2.ZFE IS INF 32Siemens AGMünchen 83Germany

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