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)


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.



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Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

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

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