Towards knowledge acquisition by experts

  • Frank Puppe
  • Ute Gappa
Knowledge Acquisition and Language Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 604)


From the three basic knowledge acquisition types for expert systems — indirect knowledge acquisition with a knowledge engineer asking an expert, direct knowledge acquisition mainly by the experts on their own, and automatic knowledge acquisition with machine learning techniques — we currently view direct knowledge acquisition as the most promising approach with respect to total project costs and the technical state of the art. Prerequisites are knowledge representations as well as problem solving methods easily comprehensible for experts and comfortable and easy to learn knowledge acquisition components. In this paper we give an overview on our research to achieve both requirements by “strong” problem solving methods and graphical knowledge acquisition facilities. This is demonstrated with a successful implementation for the well known problem class heuristic classification.


Knowledge Acquisition Expert system Classification Graphics 


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  1. Boose, J.H., & Bradshaw, J.M.: Expertise Transfer and Complex Domains: Using AQUINAS as a Knowledge Acquisition Workbench for Knowledge-Based Systems, International Journal of Man-Machine Studies, 26, 1987.Google Scholar
  2. Breuker, J.A. et al: Model-Driven Knowledge Acquisition: Interpretation Models, MEMO 87 Esprit Project 1098, University of Amsterdam, 1987.Google Scholar
  3. Chandrasekaran, B.: Towards a Taxonomy of Problem Solving Types, AI Magazine 4, No. 1, 9–17, 1983.Google Scholar
  4. Chandrasekaran, B.: Towards a Functional Architecture for Intelligence Based on Generic Information Processing Tasks, IJCAI-87, 1183–1192, 1987.Google Scholar
  5. Clancey, W.: Heuristic Classification, AI Journal 27, 289–350, 1985.Google Scholar
  6. Bamberger, S., Gappa, U., Goos, K., Meinl., A., Poeck, K., and Puppe, F.: The Classification Expert System Shell D3 (in German, translation to English in preparation). Manual. Karlsruhe University, 1991.Google Scholar
  7. Gappa, U.: CLASSIKA: A Knowledge Acquisition Tool for Use by Experts, Proceedings of the 4th Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, 1989.Google Scholar
  8. Gappa, U.: A Toolbox for Generating Graphical Knowledge Acquisition Environments, Proc. of The 1. World Congress of Expert Systems, Orlando, Vol. 2, 787–810, Pergamon Press, 1991.Google Scholar
  9. Hickam, D. et al.: The Treatment Advice of a Computer-Based Cancer Chemotherapy Protocol Adviser, Annals of Internal Medicine, 103, 928–936, 1985.PubMedGoogle Scholar
  10. Kahn, G.: TEST: A model-driven application shell. Proceedings of the Seventh Annual National Conference on Artificial Intelligence (AAAI), 1987.Google Scholar
  11. Marcus, S. (ed.): Automating Knowledge Acquisition for Expert Systems, Kluwer Academic Publishers, 1988.Google Scholar
  12. Miller, R., Pople, H., Myers, J.: INTERNIST1: An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine, New England Journal of Medicine 307, No. 8, 468–476, 1982.PubMedGoogle Scholar
  13. Musen, M. et al.: OPAL: Use of a Domain Model to Drive an Interactive Knowledge Editing Tool, International Journal of Man-Machine Studies, 26, 105–121, 1987.Google Scholar
  14. Musen, M.: Automated Generation of Model-Based Knowledge Acquisition Tools. Morgan Kaufmann Publishers, Pitman, London, 1989.Google Scholar
  15. Puppe, F.: Belief Revision for Diagnosis, in Proc. of GWAI-87, Springer, Informatik-Fachberichte 152, 175–184, 1987 (a).Google Scholar
  16. Puppe, F.: Requirements for a Classification Expert System Shell and Their Realization in MED2, Applied Artificial Intelligence, 1, 163–171, 1987 (b).Google Scholar
  17. Puppe, F.: Problem Solving Methods in Expert Systems (in German; translation to English in preparation) Springer, 1990.Google Scholar
  18. Puppe, F., Legleitner, T. and Huber, K.: DAX/MED2 — A Diagnostic Expert System for Quality Assurance of an Automatic Transmission Control Unit, in Zarri (ed.): Operational Expert Systems in Europe, Pergamon Press, 1991.Google Scholar
  19. Shaw, M. and Gaines, B.: Comparing Conceptual Structures: Consensus, Conflict, Correspondence and Contrast, Knowledge Acquisition 1, No 4, 341–363, 1989.Google Scholar
  20. Wielinga, B., Bredeweg, B. and Breuker, J.: Knowledge Acquisition for Expert Systems, Proceedings of ACAI-88, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Frank Puppe
    • 1
  • Ute Gappa
    • 2
  1. 1.Lehrstuhl für Informatik VIUniversität WürzburgWürzburgGermany
  2. 2.Institut für Logik, Komplexität und DeduktionssystemeUniversität KarlsruheKarlsruheGermany

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