Advertisement

Knowledge modelling in fuzzy expert systems

  • J. Darzentas
Section II Approaches To Uncertainty B) Fuzzy Set Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 286)

Abstract

The majority of the current knowledge based systems/expert systems (KBS/ES), decision support systems (DSS), and management information systems (MIS) have followed the traditional pattern of dealing only with crisply defined, non-fuzzy ("hard") problem situations.

This paper presents an approach to developing an overall intelligent problem solving environment, to be used by those who are faced with "soft", fuzzy, unstructured executive and administrative problems.

A suggested methodology approaches the commercial/managerial environments as human activity systems, and thus it considers the problem environment as "soft" (i.e. ill-defined). Personal, as well as consensus models of the problem situations provide the basis for problem understanding and decision support, The theory of fuzzy sets and conceptual modelling/cognitive mapping provide the framework.

An overall architecture of such an environment consists of :
  1. a)-

    an interface module for :

     
  2. i)

    human-computer communication. This interface has the task of transforming the input information which is expressed mainly in : natural language (text, descriptions, beliefs etc.); and in the form of specific domain data. This input is transformed into conceptual models and cognitive maps according to appropriate fuzzy relational data bases.

     
  3. ii)

    system-user interface for communicating the advice and general support to the decision-makers.

     
  4. b)-

    a knowledge base including an array of cognitive maps and conceptual models of various problem-owners. These represent beliefs and thinking about a range of problems specific to their organization. These models could be merged if the decision makers want to reach a consensus decision, or they could be used individually. The knowledge base can also include knowledge representation modules consisting of rules and facts.

     
  5. c)-

    an inference engine to form advice on the basis of comparing the models of the last two sections above, as well as by processing input data via standard DSS techniques if appropriate.

     

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R.E. BELLMAN, L.A. ZADEH, Decision Making in a Fuzzy Environment. Management Science, 17, 1970, B–141–B–164.Google Scholar
  2. 2.
    C.H.P. BROOKES, A Corporate Intelligent System for Soft Information Exchange. In: "Knowledge Representation for Decision Support Systems", L.B. Methlie, R.H. Sprague (Eds), IFIP, 1985, pp. 161–166.Google Scholar
  3. 3.
    B.P. BUCKLES, F.E. PETRY, Fuzzy Databases and Their Applications. In: "Fuzzy Information and Decision Processes", M.M. Gupta, E. Sanchez (Eds), North Holland, 1982.Google Scholar
  4. 4.
    J.J. BUCKLEY, Ranking Alternatives Using Fuzzy Numbers. Fuzzy Sets and Systems, 15, 1985, pp. 21–32.Google Scholar
  5. 5.
    J.J. BUCKLEY, Fuzzy Hierarchical Analysis. Fuzzy Sets and Systems, 17, 1985, pp. 233–248.Google Scholar
  6. 6.
    P.B. CHECKLAND, Systems Thinking Systems Practice, Wiley, London, 1981.Google Scholar
  7. 7.
    J. DARZENTAS, The Fuzzy Side to Some Models of Decision Making in Conflict. Submitted to the European Journal of Operational Research.Google Scholar
  8. 8.
    J. DARZENTAS, On the Use of Fuzzy Expert Systems in Office Automation. Proc. of the 7th National Conference of The Greek Operational Research society. pp. 423–432.Google Scholar
  9. 9.
    C. EDEN, S. JONES, D. SIMS, Thinking in Organizations, Mcmillan, London, 1979.Google Scholar
  10. 10.
    C. EDEN, S. JONES, D. SIMS, Messing About in Problems, Pergamon, Oxford, 1983.Google Scholar
  11. 11.
    B.R. GAINES, M.L.G. SHAW, Induction of Inference Rules for Expert Systems. Fuzzy Sets and Systems, 18, 1986, pp. 315–328.Google Scholar
  12. 12.
    P. HARMON, D. KING, Expert Systems, Wiley, 1985.Google Scholar
  13. 13.
    E. JACQUET-LAGREZE, J. SISKOS, Assessing a Set of Additive Utility Functions for Multicriteria Decision-Making: The UTA Method. European Journal of Operational Research, 10, 1982, pp. 151–164.Google Scholar
  14. 14.
    C.V. NEGOITA, Fuzzy Systems in Knowledge Engineering, Kybernetes, 14, 1985, pp. 45–49.Google Scholar
  15. 15.
    C.V. NEGOITA, Expert Systems and Fuzzy Systems, Benjamin/Cummings, California, 1985.Google Scholar
  16. 16.
    E. POLLITZER, J. JENKINS, Expert Knowledge, Expert Systems and Commercial Interests. OMEGA, 13, 1985, pp. 407–418.Google Scholar
  17. 17.
    F. H-ROTH, D.A. WATERMAN, D.B. LENAT (Eds), Building Expert Systems, Addison-Wesley, Mass., 1983.Google Scholar
  18. 18.
    M. UMANO, Retrieval from Fuzzy Database by Fuzzy Relational Algebra. IFAC Fuzzy Information, 1983.Google Scholar
  19. 19.
    L.A. ZADEH, The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I. Information Sciences, 8, 1975, pp. 199–249.Google Scholar
  20. 20.
    L.A. ZADEH, Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1, 1978, pp. 3–28.Google Scholar
  21. 21.
    L.A. ZADEH, The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems. Fuzzy Sets and Systems, 11, 1983, pp. 199–227.Google Scholar
  22. 22.
    M. ZEMANKOVA-LEECH, A. KANDEL, Fuzzy Relational Data Bases — a key to Expert Systems. Verlag TUV Rheinland, Koln, 1984.Google Scholar
  23. 23.
    H.-J. ZIMMERMANN, Using Fuzzy Sets in Operational Research. European Journal of Operational Research, 13, 1983, pp. 201–216.Google Scholar
  24. 24.
    H.-J. ZIMMERMANN, Fuzzy Set Theory and its Applications, Kluwer-Nijhoff, 1985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • J. Darzentas
    • 1
  1. 1.Department of Mathematical Sciences & ComputingPolytechnic of the South BankLondonUK

Personalised recommendations