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.
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References
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.
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.
Boose, J.H., Gaines, B.R., “Knowledge acquisition for knowledge-based systems”, AAAI 88, tutorial notes MP4, 1988.
Boose, J.H., “A survey of knowledge acquisition techniques and tools”, Knowledge Acquisition, Vol. 1 Nr. 1, p. 3 – 37, 1989.
Buchanan, B.G. et al., “Models of learning systems”, in “Encyclopedia of computer science and technology”, p. 24 – 50, Marcel Dekker, New York, 1978.
Carboneil, J.G., “Learning by analogy: Formulating and generalizing plans from past experience”, Chap. 5 [12].
Carbonell, J.G., “Derivational analogy: A theory of reconstructive problem solving and expertise acquisition”, Chap. 14 [13].
Clancey, W., presentation at the first knowledge acquisition for knowledge-based systems workshop, Banff, Canada, 1986.
Cohen, P.R., Feigenbaum, E.A. (eds.), ”The handbook of artificial intelligence”, Vol. 3, chap. 14, William Kaufmann, 1982.
DeJong, G., Mooney, R., “Explanation-based learning: An alternative view”, Machine Learning 1, p. 145 – 176, 1986.
Ellman, T., “Explanation-based learning: A survey of programs and perspectives”, ACM computing surveys, Vol. 21, Nr. 2, p. 163 – 221, June 1989.
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).
Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.), “Machine learning: An artificial intelligence approach”, Vol. 2, Morgan Kaufmann, 1986.
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.
Mitchell, T.M., Keller, R.M., Kedar-Cabelli, S.T., “Explanation-based learning: A unifying view”, Machine Learning 1, p. 47 – 80, 1986.
Pazzani, M.J., “Integrating explanation-based and empirical learning methods in OCCAM”, proceedings of the third European working session on learning (EWSL), 1988.
Pearce, D.A., “The induction of fault diagnosis systems from qualitative models”, p. 353–357, proceedings AAAI 88, 1988.
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, 1987
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.
Simon, H.A., “Why should machines learn?”, Chap. 2, [12].
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© 1990 Springer-Verlag Berlin Heidelberg
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Zercher, K., Radig, B. (1990). The Role of Machine Learning in Knowledge Acquisition. In: Gasteiger, J. (eds) Software Development in Chemistry 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-75430-2_37
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DOI: https://doi.org/10.1007/978-3-642-75430-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-52173-0
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