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Using generalised directive models in knowledge acquisition

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Current Developments in Knowledge Acquisition — EKAW '92 (EKAW 1992)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 599))

Abstract

In this paper we describe Generalised Directive Models and their instantiation in the ACKnowledge Knowledge Engineering Workbench. We have developed a context sensitive rewrite grammar that allows us to capture a large class of inference layer models. We use the grammar to progressively refine the model of problem solving for an application. It is also used as the basis of the scheduling of KA activities and the selection of KA tools.

The research reported here was carried out in the course of the ACKnowledge project partially founded by the ESPIRIT Programme of the Commission of the European Communities as project number 2567. The Partners in this project are GEC-Marconi, University of Nottingham (both UK), Cap Gemini Innovation (F), Sintef, Computas Expert Systems (both N), Telefonica (ES), and the University of Amsterdam (NL).

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References

  1. G. Aakvik, A. Aamodt, and I. Norbo. A knowledge representation framework supporting knowledge modelling. In Proceedings of the fifth EKAW, University of Strathclyde, 1991. Not Published.

    Google Scholar 

  2. A. Anjewierden, J. Wielemaker, and C. Toussaint. Shelley — computer aided knowledge engineering. In B. Wielinga, J. Boose, B. Gaines, G. Schreiber, and M. van Someren, editors, Current Trends in Knowledge Acquisition, pages 173–189. IOS Press, Amsterdam, 1990.

    Google Scholar 

  3. V.R. Benjamins, A. Abu-Hanna, and Wouter Jansweijer. Dynamic method selection in diagnostic reasoning. In submitted to the 12th Int. conf. on Artificial Intelligence, Expert Systems, Natural Language, Avignon'92, Avignon, 1992. EC2. SKBS/A2/92-1.

    Google Scholar 

  4. B. Chandrasekaran. Towards a functional architecture for intelligence based on generic information processing tasks. In Proceedings of the 10th IJCAI, pages 1183–1192, Milano, 1987.

    Google Scholar 

  5. B. Chandrasekaran. Design problem solving: a task analysis. AI Magazine, Winter:59–71, 1990.

    Google Scholar 

  6. W.J. Clancey. Heuristic classification. Artificial Intelligence, 27:289–350, 1985.

    Article  Google Scholar 

  7. L. Eshelman. MOLE: A knowledge-acquisition tool for cover-and-differentiate systems. In S. Marcus, editor, Automating Knowledge Acquisition for Expert Systems, pages 37–80. Kluwer Academic Publishers, The Netherlands, 1988.

    Google Scholar 

  8. C. Jullien et al. Acknowledge final report. Technical Report ACK-CSI-WM-DL-007-A-pre4, Cap Gemini Innovation, 1992. Final report of ESPRIT Project 2567.

    Google Scholar 

  9. W. Karbach, M. Linster, and A. Voß. Model-based approaches: One label — one idea? In B. Wielinga, J. Boose, B. Gaines, G. Schreiber, and M. van Someren, editors, Current Trends in Knowledge Acquisition, pages 173–189. IOS Press, Amsterdam, 1990.

    Google Scholar 

  10. N. Major and H. Reichgelt. Alto: an automated laddering tool. In B. Wielinga, J. Boose, B. Gaines, G. Schreiber, and M. van Someren, editors, Current trends in knowledge acquisition, pages 222–236, Amsterdam, May 1990. IOS Press.

    Google Scholar 

  11. S. Marcus and J. McDermott. SALT: A knowledge acquisition language for propose-and-revise systems. Artificial Intelligence, 39(1):1–38, 1989.

    Article  Google Scholar 

  12. D. Marques, G. Dallemagne, G. Klinker, J. McDermott, and D. Tung. Easy programming: Empowering people to build their own applications. Technical report, Digital Equipment Corporation, 1991. submitted to the IEEE Expert.

    Google Scholar 

  13. 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 Academic Publishers, The Netherlands, 1989.

    Google Scholar 

  14. M.A. Musen. Automated Generation of Model-Based Knowledge-Acquisition Tools. Pitman, London, 1989. Research Notes in Artificial Intelligence.

    Google Scholar 

  15. M.A. Musen, L.M. Fagan, D.M. Combs, and E.H. Shortliffe. Use of a domain model to drive an interactive knowledge editing tool. In J. Boose and B. Gaines, editors, Knowledge-Based Systems, Volume 2: Knowledge Acquisition Tools for Expert Systems, pages 257–273, London, 1988. Academic Press.

    Google Scholar 

  16. A. Puerta, J. Egar, S. Tu, and M. Musen. A multiple-method knowledge acquisition shell for the automatic generation of knowledge acquisition tools. Technical Report KSL-91-24, Stanford University, Knowledge Systems Laboratory, Medical Computer Science, Stanford University, Stanford, California, 94305-5479, May 1991.

    Google Scholar 

  17. H. Reichgelt. Logic-Based Knowledge Representation, chapter Assertion Time Inference. The MIT Press, Cambridge, MA, 1989.

    Google Scholar 

  18. 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, 1990. IOS Press.

    Google Scholar 

  19. L. Steels. Components of expertise. AI Magazine, Summer 1990. Also as: AI Memo 88–16, AI Lab, Free University of Brussels.

    Google Scholar 

  20. F. van Harmelen and J. Balder. (ML)2: A formal language for kads models of expertise. Technical Report ESPRIT Project P5248 KADS-II/T1.2/PP/UvA/017/1.0, University of Amsterdam & Netherlands Energy Research Foundation ECN, 1991. This paper is published in Knowledge Acquisition Journal, 1992.

    Google Scholar 

  21. J. Vanwelkenhuysen and P. Rademakers. Mapping knowledge-level analysis onto a computational framework. In L. Aiello, editor, Proceedings ECAI-90, Stockholm, pages 681–686, London, 1990. Pitman.

    Google Scholar 

  22. B. J, Wielinga, A. Th. Schreiber, and J. A. Breuker. KADS: A modelling approach to knowledge engineering. Knowledge Acquisition, 1992. Special issue on KADS, forthcoming. Also as: Technical Report ESPRIT Project P5248 KADS-II/T1.1/PP/UvA/008/2.0, University of Amsterdam.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Social Science Informatics, Department of Psychology, University of Amsterdam, Roeterstraat 15, 1018, WB Amsterdam, The Netherlands

    Gertjan van Heijst, Peter Terpstra & Bob Wielinga

  2. AI Group, Department of Psychology, University Park, NG7 2RD, Nottingham University, Nottingham, UK

    Nigel Shadbolt

Authors
  1. Gertjan van Heijst
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  2. Peter Terpstra
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  3. Bob Wielinga
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  4. Nigel Shadbolt
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Editor information

Thomas Wetter Klaus-Dieter Althoff John Boose Brian R. Gaines Marc Linster Franz Schmalhofer

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© 1992 Springer-Verlag Berlin Heidelberg

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van Heijst, G., Terpstra, P., Wielinga, B., Shadbolt, N. (1992). Using generalised directive models in knowledge acquisition. In: Wetter, T., Althoff, KD., Boose, J., Gaines, B.R., Linster, M., Schmalhofer, F. (eds) Current Developments in Knowledge Acquisition — EKAW '92. EKAW 1992. Lecture Notes in Computer Science, vol 599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55546-3_37

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  • DOI: https://doi.org/10.1007/3-540-55546-3_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55546-9

  • Online ISBN: 978-3-540-47203-2

  • eBook Packages: Springer Book Archive

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