Combining Both a Fuzzy Inductive Learning and a Fuzzy Repertory Grid Method

  • J. L. Castro
  • J. J. Castro-Schez
  • J. M. Zurita
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 89)


In this paper we describe a new approach to the problem of management of the inconsistency in expert systems which can be used for acquiring knowledge. The method proposed is used for planning interviews with the domain expert. The validation method searches for inconsistent areas in the knowledge base and asks the expert questions with the aim of resolving the conflict present in these areas. The questions asked will depend on the area in which the inconsistency arises.


Knowledge Base Expert System Domain Expert Control Rule Knowledge Engineer 
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]
    Castro, J.L, and Trillas, E. The management of the inconsistency in expert systems. Fuzzy Sets and Systems, 58: 51–57, 1993.MathSciNetCrossRefGoogle Scholar
  2. [2]
    Castro, J.L, Trillas, E. and Zurita, J.M. Searching potential conflicts in medical expert systems. Fuzzy Sets and AI, Vol 3 (1): 79–94, 1994.Google Scholar
  3. [3]
    Castro, J.L., Castro-Schez, J.J., and Zurita, J.M. Fuzzy Repertory Table, a method for acquiring knowledge about input variables to machine learning algorithms (in review).Google Scholar
  4. [4]
    Castro, J.L. Castro-Schez, J.J., and Zurita, J.M. Learning Maximal Structure Rules in Fuzzy Logic for Knowledge Acquisition in Expert Systems. Fuzzy Sets and Systems, 101: 331–342, 1999.MathSciNetMATHGoogle Scholar
  5. [5]
    Fisher, R.A. The use of multiple measurements in taxonomic problems. Annual Engenics, 7–11: 179–188.Google Scholar
  6. [6]
    Larsen, H.L., and Nonfjall, H. Modelling in the design of a KBS verification system. International Journal of Intelligence System, 1991.Google Scholar
  7. [7]
    Meseguer, P. and Verdaguer, A. Verification of Multi-Level Rule-Based Expert Systems: Theory and Practice. International Journal of Expert Systems. Vol. 6 (2): 163–192, 1993.Google Scholar
  8. [8]
    Nguyen, T.A., Perkins, W.A., Laffey, T.J., and Pecora, D. Knowledge base verification. AI Magazine 8 (2): 69–79, 1987.Google Scholar
  9. [9]
    Rousset, M. On the consistency of knowledge bases: The COVADIS system. In Proc. of ECAI-88, Munich, Germany, 79–84, 1988.Google Scholar
  10. [10]
    Tecuci, G. Automating Knowledge Acquisition as Extending, Updating and Improving a Knowledge Base. IEEE Transactions on Systems, Man and Cybernetics, 22 (6): 1444–1460, 1992.CrossRefGoogle Scholar
  11. [11]
    Yager, R.R., and Larsen, H.L. On discovering potential inconsistencies in validating uncertain knowledge bases by reflection on the input. Tech. Report #MII-100, 1990 or In Proc. Verification, Validation, and Testing of KBS Workshop of the AAAI-91 Conference.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. L. Castro
    • 1
  • J. J. Castro-Schez
    • 2
  • J. M. Zurita
    • 1
  1. 1.Dpto. Ciencias de la Computación e I.A. E.T.S.I. InformáticaUniversidad de GranadaGranadaSpain
  2. 2.Dpto. Informatica Escuela Universitaria de InformáticaUniversidad de Castilla-LaCiudad RealSpain

Personalised recommendations