On a role of problem solving methods in knowledge acquisition

Experiments with diagnostic strategies
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 867)


Libraries with re-usable knowledge components are becoming increasingly important in Knowledge Acquisition. We propose a library of problem solving methods for diagnosis and describe some experiments and results concerning the usefulness of such a library for constructing and analyzing diagnostic strategies. A key notion is that each problem solving method is associated with suitability criteria, which are exploited in the process.


Knowledge Acquisition Diagnostic Strategy Domain Characteristic Knowledge Engineer Suitability Criterion 
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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  1. 1.
    A. Aamodt, B. Benus, C. Duursma, C. Tomlinson, R. Schrooten, and W. Van de Velde. Task features and their use in commonkads. Technical Report KADS-II/T1.5/VUB/TR/014/1.0, Free University of Brussels & University of Amsterdam & Lloyd's Register, 1992.Google Scholar
  2. 2.
    A. Abu-Hanna. Multiple domain models in diagnostic reasoning. PhD thesis, University of Amsterdam, Amsterdam, 1994.Google Scholar
  3. 3.
    V. R. Benjamins. Problem Solving Methods for Diagnosis. PhD thesis, University of Amsterdam, Amsterdam, The Netherlands, June 1993.Google Scholar
  4. 4.
    V. R. Benjamins and A. Abu-Hanna. FAULTY: A shell for diagnosing complex technical systems. Technical Report SKBS/A2/90-1, SWI, University of Amsterdam, Amsterdam, 1990.Google Scholar
  5. 5.
    V. R. Benjamins and W. N. H. Jansweijer. Towards a competence theory of diagnosis. IEEE-Expert, 9(4), august 1994.Google Scholar
  6. 6.
    B. Bredeweg. Model-based diagnosis and prediction of behaviour. Technical Report KADS-II/M2/UvA/1.0, SWI, University of Amsterdam, Amsterdam, 1994.Google Scholar
  7. 7.
    J. A. Breuker, B. J. Wielinga, M. van Someren, R. de Hoog, A. Th. Schreiber, P. de Greef, B. Bredeweg, J. Wielemaker, J. P. Billault, M. Davoodi, and S. A. Hayward. Model Driven Knowledge Acquisition: Interpretation Models. ESPRIT Project P1098 Deliverable D1 (task A1), University of Amsterdam and STL Ltd, 1987.Google Scholar
  8. 8.
    B. Chandrasekaran. Generic tasks as building blocks for knowledge-based systems: The diagnosis and routine design examples. The Knowledge Engineering Review, 3(3):183–210, 1988.Google Scholar
  9. 9.
    B. Chandrasekaran. Design problem solving: A task analysis. AI Magazine, 11:59–71, 1990.Google Scholar
  10. 10.
    B. Chandrasekaran, T. R. Johnson, and J. W. Smith. Task-structure analysis for knowledge modeling. Communications of the ACM, 35(9):124–137, 1992.Google Scholar
  11. 11.
    L. Console and P. Torasso. Hypothetical reasoning in causal models. Int. J. of Intelligent Systems, 5(1):83–124, 1990.Google Scholar
  12. 12.
    R. Davis and W. C. Hamscher. Model-based reasoning: Troubleshooting. In H. E. Shrobe, editor, Exploring Artificial Intelligence, pages 297–346. Morgan Kaufmann, San Mateo, California, 1988.Google Scholar
  13. 13.
    J.H. de Kleer and B.C. Williams. Diagnosing multiple faults. Artificial Intelligence, 32:97–130, 1987.Google Scholar
  14. 14.
    M. R. Genesereth. The use of design descriptions in automated diagnosis. Artificial Intelligence, 24:411–436, 1984.Google Scholar
  15. 15.
    K. Orsvärn. Towards problem solving methods for sequential diagnosis. Technical Report KADS-II/M2.3/TR/SICS/001/1.0, SICS, 1994.Google Scholar
  16. 16.
    G. Klinker, C. Bhola, G. Dallemagne, D. Marques, and J. McDermott. Usable and reusable programming constructs. Knowledge Acquisition, 3:117–136, 1991.Google Scholar
  17. 17.
    J. McDermott. Preliminary steps towards a taxonomy of problem-solving methods. In S. Marcus, editor, Automating Knowledge Acquisition for Expert Systems, pages 225–255. Kluwer, Boston, 1988.Google Scholar
  18. 18.
    A. Newell. The knowledge level. Artificial Intelligence, 18:87–127, 1982.Google Scholar
  19. 19.
    A. Puerta, J. Egar, S. W. Tu, and M. A. Musen. A multiple-method knowledge-acquisition shell for the automatic generation of knowledge-acquisition tools. In Proc. 6th Banff Knowledge Acquisition Workshop, pages 20.1–19, Canada, 1991. SRDG Publications, University of Calgary.Google Scholar
  20. 20.
    W.F. Punch. A Diagnosis System Using a Task Integrated Problem Solver Architecture (TIPS), Including Causal Reasoning. PhD thesis, The Ohio State University, Ohio, 1989.Google Scholar
  21. 21.
    A. Th. Schreiber, B. J. Wielinga, and J. A. Breuker, editors. KADS: A Principled Approach to Knowledge-Based System Development, volume 11 of Knowledge-Based Systems Book Series. Academic Press, London, 1993.Google Scholar
  22. 22.
    L. Steels. Components of expertise. AI Magazine, Summer 1990.Google Scholar
  23. 23.
    P. Struss and O. Dressler. Physical negation — integrating fault models into the general diagnostic engine. In Proc 11th. IJCAI, pages 1318–1323, Detroit, 1989.Google Scholar
  24. 24.
    A. Valente, B. Bredeweg, J. Breuker, and W. van de Velde. A library of re-usable knowledge models and components. In Proc. of the Conference of the Brazilian Computing Society, Florianópolis, august 1993.Google Scholar
  25. 25.
    G. van Heijst, P. Terpstra, B. J. Wielinga, and N. Shadbolt. Using generalised directive models in knowledge acquisition. In Th. Wetter, K. D. Althoff, J. Boose, B. Gaines, M. Linster, and F. Schmalhofer, editors, Current Developments in Knowledge Acquisition: EKAW-92, Berlin, Germany, 1992. Springer-Verlag.Google Scholar
  26. 26.
    J. Vanwelkenhuysen and P. Rademakers. Mapping knowledge-level analysis onto a computational framework. In L. Aiello, editor, Proc. ECAI-90, pages 681–686, London, 1990. Pitman.Google Scholar
  27. 27.
    B. J. Wielinga, W. Van de Velde, A. Th. Schreiber, and J. M. Akkermans. The Common KADS framework for knowledge modelling. In B. R. Gaines, M. A. Musen, and J. H. Boose, editors, Proc. 7th Banff Knowledge Acquisition Workshop, volume 2, pages 31.1–31.29. SRDG Publications, University of Calgary, Alberta, Canada, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  1. 1.Laboratory of Integrated SystemsEscola Politécnica of the University of São Paulo (EPUSP)Sõ PauloBrazil

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