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

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Technologies for Constructing Intelligent Systems 1

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 89))

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Abstract

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.

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References

  1. Castro, J.L, and Trillas, E. The management of the inconsistency in expert systems. Fuzzy Sets and Systems, 58: 51–57, 1993.

    Article  MathSciNet  Google Scholar 

  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. 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. 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.

    MathSciNet  MATH  Google Scholar 

  5. Fisher, R.A. The use of multiple measurements in taxonomic problems. Annual Engenics, 7–11: 179–188.

    Google Scholar 

  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. 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. 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. Rousset, M. On the consistency of knowledge bases: The COVADIS system. In Proc. of ECAI-88, Munich, Germany, 79–84, 1988.

    Google Scholar 

  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.

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Dpto. Ciencias de la Computación e I.A. E.T.S.I. Informática, Universidad de Granada, Avenida de Andalucía, 38, 18071, Granada, Spain

    J. L. Castro & J. M. Zurita

  2. Dpto. Informatica Escuela Universitaria de Informática, Universidad de Castilla-La, Mancha Ronda de Calatrava, 5, 13071, Ciudad Real, Spain

    J. J. Castro-Schez

Authors
  1. J. L. Castro
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  2. J. J. Castro-Schez
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  3. J. M. Zurita
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Editor information

Editors and Affiliations

  1. CNRS, LIP6, Université Paris VI, 8 rue du Capitaine Scott, 75015, Paris, France

    Bernadette Bouchon-Meunier

  2. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Madrid, Spain

    Julio Gutiérrez-Ríos

  3. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain

    Luis Magdalena

  4. Machine Intelligence Institute, Iona College, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2002 Springer-Verlag Berlin Heidelberg

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Castro, J.L., Castro-Schez, J.J., Zurita, J.M. (2002). Combining Both a Fuzzy Inductive Learning and a Fuzzy Repertory Grid Method. In: Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., Yager, R.R. (eds) Technologies for Constructing Intelligent Systems 1. Studies in Fuzziness and Soft Computing, vol 89. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1797-3_21

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  • DOI: https://doi.org/10.1007/978-3-7908-1797-3_21

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-00329-9

  • Online ISBN: 978-3-7908-1797-3

  • eBook Packages: Springer Book Archive

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