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Abstract

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.

Keywords

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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References

  1. (1).
    Buchanan, B.G. and Duda, R.O., “Principles of rule-based expert systems,” Fairchild Technical Report No. 626, Lab. for Artificial Intelligence Research, Fairchild Camera, Palo Alto, Ca., 1982.Google Scholar
  2. (2).
    Buchanan, B.G., “Partial bibliography of work on expert systems,” Sigart Newsletter, No. 84, 45–50, 1983.Google Scholar
  3. (3).
    Shortliffe, E.H., Computer Based Medical Consultations: MYCIN, American Elsevier, New York 1976.Google Scholar
  4. (4).
    Davis, R., Buchanan, B.G. & Shortliffe, E.H., “Production rules as a representation of a knowledge-based consultation program,” Artificial Intelligence 8, 15–45,1977.zbMATHCrossRefGoogle Scholar
  5. (5).
    Van Melle, W., “A domain independent system that aids in constructing knowledge-based consultation program,” Ph.D. dissertation, Stanford University Computer Science Dept., Stanford CS-80–820, 1980.Google Scholar
  6. (6).
    Zadeh, L.A., “Fuzzy logic and approximate reasoning,” Synthese 30, 407–428, 1975.zbMATHCrossRefGoogle Scholar
  7. (7).
    Zadeh, L.A., “The concept of a linguistic variable and its application to approximate reasoning,” Information Science 8 and 9, 199–249, 301–357, 43–80, 1975.Google Scholar
  8. (8).
    Zadeh, L.A., “A theory of approximate reasoning,” in Hayes, J.E., Michie, D. and Kulich, L.I., (eds) Machine Intelligence 9, 149–194, John Wiley & Sons, New York, 1979.Google Scholar
  9. (9).
    Zadeh, L.A., “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems 1, 3–28, 1978.MathSciNetzbMATHCrossRefGoogle Scholar
  10. (10).
    Zadeh, L.A., “PRUF-a meaning representation language for natural languages,” Int. J. of Man-Machine Studies 10, 395–460, 1978.MathSciNetzbMATHCrossRefGoogle Scholar
  11. (11).
    Yager, R.R., “Querying knowledge base systems with linguistic information via knowledge trees,” Int. J. Man- Machine Studies 19, 1983.Google Scholar
  12. (12).
    Yager, R. R., “Knowledge trees in complex knowledge bases,” Fuzzy Sets and Systems 15, 45–64, 1985.MathSciNetzbMATHCrossRefGoogle Scholar
  13. (13).
    Zadeh, L.A., “Fuzzy sets,” Information and Control 8, 338–353,1965.MathSciNetzbMATHCrossRefGoogle Scholar
  14. (14).
    Zadeh, L.A., “A computational approach to fuzzy quantifiers in natural languages,” Com. $ Maths. with Appl. 9, 149–184, 1983.MathSciNetzbMATHGoogle Scholar
  15. [15]
    Yager, R. R., “Quantifiers in the formulation of multiple objective decision functions,” Information Sciences 31, 107–139, 1983.MathSciNetzbMATHCrossRefGoogle Scholar
  16. [16]
    Yager, R. R., “Default and approximate reasoning,” Proc. 2nd IFSA Conference, Tokyo, 690–692, 1987.Google Scholar
  17. [17]
    Yager, R. R., Ovchinnikov, S., Tong, R. and Nguyen, H., Fuzzy Sets and Applications: Selected Papers by L. A. Zadeh, John Wiley & Sons: New York, 1987.Google Scholar
  18. [18]
    Yager, R. R., “Nonmonotonic inheritance systems,” IEEE Transactions on Systems, Man and Cybernetics 18, 1028–1034, 1988.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Yager, R. R., “On the representation of commonsense knowledge by possibilistic reasoning,” Int. J. of Man-Machine Systems 31, 587–610, 1989.CrossRefGoogle Scholar
  20. [20]
    Reiter, R., “A logic for default reasoning,” Artificial Intelligence 13, 81–132, 1980.MathSciNetzbMATHCrossRefGoogle Scholar

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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