Generic Tasks in KEW

Problem Solving Models Comparison of Approaches
Part of the Lecture Notes in Computer Science book series (LNCS, volume 723)


In this paper we describe an experiment in which we cast Generic Tasks into the mold provided by KEW. The result was advantageous for both GT theory and KEW-GT benefitted by being formalized, and by gaining a computer implementation. KEW benefitted by having its strategy vindicated on a new target theory, and having its software more thoroughly tested. The experiment also exposed a weakness in the KEW meta-methodology, which might have implications beyond its use in KEW.


Design Task Knowledge Acquisition Task Analysis Knowledge Source Generic Task 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dean Allemang. Sisyphus'91, generic tasks. In Sisyphus Working Papers Part 2: Models of problem solving, 1991.Google Scholar
  2. 2.
    Dean Allemang and Thomas Rothenfluh. Acquiring knowledge of knowledge acquisition: a self-study of generic tasks. In Wetter, Althoff, Boose, Gaines, Linster, and Schmalhofer, editors, Current Developments in Knowledge Acquisition, pages 353–371, 1992.Google Scholar
  3. 3.
    A. Anjewierden, J. Wielemaker, and C. Toussaint. Shelley — computer aided knowledge engineering. Knowledge Acquisition Journal, 4(1), 1992. Special issue: “The KADS approach to knowledge engineering”.Google Scholar
  4. 4.
    D. C. Brown and B. Chandrasekaran. Design Problem Solving: Knowledge structures and control strategies. Morgan Kaufmann, San Mateo, CA., 1989.Google Scholar
  5. 5.
    Tom Bylander and Sanjay Mittal. Csrl: A language for classifacatory problem solving. AI VII, 3:66–77, 1986.Google Scholar
  6. 6.
    B. Chandrasekaran. Design problem solving: A task analysis. AI Magazine, 11(4):59–71, 1990.Google Scholar
  7. 7.
    B. Chandrasekaran and Todd R. Johnson. Generic tasks and task structures: History, critique and new directions,. In J. M. David, J. P. Krivine, and R. Simmons, editors, Second Generation Expert Systems. Springer Verlag, 1993.Google Scholar
  8. 8.
    David Herman. DSPL++: A high-level language for building design expert systems with flexible use of multiple methods. PhD thesis, The Ohio State University, 1990.Google Scholar
  9. 9.
    John R. Josephson, Diana Smetters, Richard Fox, Dan Oblinger, Arun Welch,-and Gayle Northrup. Integrated generic task toolset. Ohio State University Tech Report, 1988.Google Scholar
  10. 10.
    E. Motta, T. Rajan, and M. Eisenstadt. A Methodology and Tool for Knowledge Acquisition in Keats-2. In P. Guida and G. Tasso, editors, Topics in the Design of Expert Systems, pages 265–296, Amsterdam, The Netherlands, 1989. North-Holland.Google Scholar
  11. 11.
    Hyacinth Nwana. Using kads and generic tasks to model a timetabling problem. In EKAW'93. Proceedings of the Seventh European Workshop on Knowledge Acquisition, 1993.Google Scholar
  12. 12.
    Kieron Ohara and Nigel Shadbolt. Comments on chandrasekaran. Presented at AAAI Symposium on Knowledge Level Modelling, 1992.Google Scholar
  13. 13.
    A. Th. Schreiber. Pragmatics of the Knowledge Level. PhD thesis, University of Amsterdam, October 1992.Google Scholar
  14. 14.
    N. Shadbolt and B. J. Wielinga. Knowledge based knowledge acquisition: the next generation of support tools. In B. J. Wielinga, J. Boose, B. Gaines, G. Schreiber, and M. W. van Someren, editors, Current Trends in Knowledge Acquisition, pages 313–338, Amsterdam, The Netherlands, 1990. IOS Press.Google Scholar
  15. 15.
    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
  16. 16.
    B. J. Wielinga, A. Th. Schreiber, and J. A. Breuker. KADS: A modelling approach to knowledge engineering. Knowledge Acquisition Journal, 4(1), 1992. Special issue ‘The KADS approach to knowledge engineering'.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Swiss Federal Institute of TechnologyLausanneSwitzerland
  2. 2.University of AmsterdamWB Amsterdam

Personalised recommendations