Knowledge acquisition by inductive learning from examples

  • Joachim Selbig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 265)


Before we describe our approach to the problem of learning the action part of IF(pattern) THEN-DO(action)-rules we give a survey to the problem of knowledge acquisition for expert systems. In connection with our work we focus on automatic knowledge acquisition by learning methods.


Knowledge Engineer Automatic Knowledge 
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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6. References

  1. [1]
    Bonnet, A. Schema-Shift Strategies for Understanding Texts in Natural Language, Stanford University Technical Report HPP-25, 1979Google Scholar
  2. [2]
    Bundy,A., Silver,B., Plummer,D. An Analytical Comparison of Some Rule Learning Programs, University of Edinburgh D.A.I. Technical Report No. 215, 1984Google Scholar
  3. [3]
    Cohen,P.R., Feigenbaum,E.A. (Eds.) The Handbook of Artificial Intelligence, Vol. III, Pitman Books Ltd., 1982Google Scholar
  4. [4]
    Davis, R. Interactive Transfer of Expertise: Acquisition of New Inference Rules, Artificial Intelligence, 12,1979, 121–157Google Scholar
  5. [5]
    Feigenbaum,E.A. Knowledge Engineering, The Applied Side of Artificial IntelligenceGoogle Scholar
  6. [6]
    Ganascia,J.G. Reasoning And Result in Expert Systems: Main Differences between Diagnostic Systems And Problem Solvers, in: Proc. ECAI-84, 31–40Google Scholar
  7. [7]
    Greiner,R. RLL-1: A Representation Language Language, Stanford University Technical Report HPP-9, 1980Google Scholar
  8. [8]
    Haugeneder,H., Lehmann,E., Struss,P. Knowledge-Based Configuration of Operating Systems — Problems in Modeling the Domain Knowledge, in: Brauer,W., Radig,B. (Hrsg.) Informatik-Fachberichte 112 Springer-Verlag, 1985, 121–134Google Scholar
  9. [9]
    Hinteregger, J., Tinhofer, G. Zerlegung der Knotenmengen von Graphen zum Nachweis der Isomorphie, Computing, 18,1977, 351–359Google Scholar
  10. [10]
    Holte,R.C. Artificial Intelligence Approaches to Concept Learning in: Aleksander,I. (Ed.) Advanced Digital Information Systems, Prentice-Hall, 1985, 309–499Google Scholar
  11. [11]
    Horn,W. Knowledge Engineering: Werkzeuge zum Erstellen von Expertensystemen, in: Hansen, H.R. (Hrsg.) Informatik-Fachberichte 108, Springer-Verlag, 1985, 64–75Google Scholar
  12. [12]
    Kaden, F. Zur Formalisierung Induktiver Schluesse ueber Strukturierten Objekten, ZKI-Information, Berlin, 3, 1980Google Scholar
  13. [13]
    Lenat, D. AM: Discovery in Mathematics as Heuristic Search, PhD thesis, Stanford University, 1977Google Scholar
  14. [14]
    Michalski, R.S., Davis, J.H., Bisht, V.S., Sinclair, J.B. PLANT/DS: An Expert Consulting System for The Diagnosis of Soybean Diseases, in: Proc. ECAI-82, 139–140Google Scholar
  15. [15]
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M. Machine Learning, An Artificial Intelligence Approach, Springer-Verlag, 1984Google Scholar
  16. [16]
    Nielson, N.J. Principles of Artificial Intelligence, Tioga, 1980Google Scholar
  17. [17]
    Rollinger, C.-R., Schneider, H.-J. Textunderstanding as A Knowledge-Based Approach, in: Deutschmann, F. (Ed.) Representation And Exchange of Knowledge as A Bases of Information Processes, North-Holland, 1984, 129–142Google Scholar
  18. [18]
    Selbig, J. Representation And Generalisation of Transformations between Relational Structures, in: Plander, I. (Ed.) Proc. AIICS-84, 325–328 North-Holland, 1984Google Scholar
  19. [19]
    Sell, P.S. Expert Systems — A Practical Introduction, MACMILLAN Publ. Ltd., 1985Google Scholar
  20. [20]
    Sobik, F., Sommerfeld, E. A Graph-Theoretic Approach for Representation And Classification of Structured Objects, in: Proc. ECAI-82Google Scholar
  21. [21]
    Steels, L. Design Requirements for Knowledge Representation Systems, in: Laubsch, J. (Hrsg.) Informatik-Fachberichte 103, Springer-Verlag, 1985, 1–19Google Scholar
  22. [22]
    Unger, S., Wysotzki, F. Lernfaehige Klassifizierungssystems, Akademie-Verlag, 1981Google Scholar
  23. [23]
    Utgoff, P.E. Machine Learning of Inductive Bias, KLUWER Academic Publishers, 1986Google Scholar
  24. [24]
    Walker, A. Knowledge Systems: Principles And Practice, IBM Res. Develop. 30, 1986, 2–13Google Scholar
  25. [25]
    Waterman, D.A. A Guide to Expert Systems, Addison-Wesley Publ. Comp., 1986Google Scholar
  26. [26]
    Wysotzki, F., Kolbe, W., Selbig, J. Concept Learning by Structured Examples — An Algebraic Approach, in: Proc. IJCAI-81, 153–158Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Joachim Selbig
    • 1
  1. 1.Department of Artificial IntelligenceCentral Institute of Cybernetics and Information ProcessesBerlinDDR

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