We show how the theory of approximate reasoning developed by L.A. Zadeh provides a natural format for representing the knowledge and performing the inferences in rule based expert systems. We extend the representational ability of these systems by providing a new structure for including rules which only require the satisfaction to some subset of the antecedent conditions. This is accomplished by the use of fuzzy quantifiers. We also provide a methodology for the inclusion of a form of uncertainty in the expert systems associated with the belief attributed to the data and production rules.


Expert System Fuzzy Subset Reasoning System Membership Grade Possibility Distribution 
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.


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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Ronald R. Yager
    • 1
  1. 1.Machine Intelligence InstituteIona CollegeNew RochelleUSA

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