Skip to main content

A knowledge level model of knowledge-based reasoning

  • Selected Papers
  • Conference paper
  • First Online:
Topics in Case-Based Reasoning (EWCBR 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 837))

Included in the following conference series:

Abstract

We propose to analyze CBR systems at knowledge level following the Components of Expertise methodology. This methodology has been used for design and construction of KBS applications. We have applied it to analyze learning methods of existing systems at knowledge level. As example we develop the knowledge level analysis of CHEF. Then a common task structure of CBR systems is explained. We claim that this sort of analysis can be a first step to integrate different learning methods into case-based reasoning systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Aamodt: A knowledge-intensive, integrated approach to problem solving and sustained learning. Ph. D. Dissertation. University of Trondheim (1991)

    Google Scholar 

  2. J. L. Arcos, E. Plaza: A reflective architecture for integrated memory-based learning and reasoning. European Workshop on Case-based Reasoning EWCBR'93

    Google Scholar 

  3. E. Armengol, E. Plaza: Analyzing case-based reasoning at the knowledge level. Research Report IIIA 93/14 (1993)

    Google Scholar 

  4. R. Bareiss: Exemplar-based knowledge acquisition. A unified approach to concept representation, classification and learning. Perspectives in Artificial Intelligence. Volume 2. Academic Press Inc. 1989.

    Google Scholar 

  5. T.G. Dietterich: Learning at the knowledge level. Machine Learning 3, 287–354 (1986).

    Google Scholar 

  6. K.J. Hammond: Case-based planning. Viewing planning as a memory task. Perspectives in Artificial Intelligence. Volume 1. Academic Press, Inc. 1989.

    Google Scholar 

  7. P. Koton: Reasoning about evidence in causal explanations. Proceedings of the CBR Workshop (DARPA). (1988).

    Google Scholar 

  8. W.J. Long, S. Naimi, M.G. Criscitiello, and R. Jayes: Using a physiological model for prediction of therapy effects in heart disease. In: Proceedings of the Computers in Cardiology Conference, IEEE, October. (1986)

    Google Scholar 

  9. A. Newell: The knowledge level. Artificial Intelligence 18, 87–127 (1982).

    Google Scholar 

  10. L. Steels: Reusability and configuration of applications by non-programmers. VUB AI-Lab Research Report (1992)

    Google Scholar 

  11. W. Van de Velde: Issues in knowledge level modelling. J. M. David, J. P. Krivine and R. Simmons (Eds.) Second Generation Expert Systems. Springer Verlag Berlin.

    Google Scholar 

  12. B. Wielinga, A. Schreiber, J. Breuker: KADS: A modelling approach to knowledge engineering. Knowledge Acquisition 4(1) (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Stefan Wess Klaus-Dieter Althoff Michael M. Richter

Rights and permissions

Reprints and permissions

Copyright information

© 1994 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Armengol, E., Plaza, E. (1994). A knowledge level model of knowledge-based reasoning. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_76

Download citation

  • DOI: https://doi.org/10.1007/3-540-58330-0_76

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58330-1

  • Online ISBN: 978-3-540-48655-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics