On the Completion of Qualitative Possibility Measures

IEEE Transactions on Fuzzy Systems

Volumn 1, Issue 3, 1993, Pages 184-194

  Yager, Ronald R ^a  

^a MACHINE INTELLIGENCE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NUMERICAL ANALYSIS; PROBABILITY;

BUOYANCY MEASURES; QUALITATIVE POSSIBILITY MEASURES;

BOOLEAN ALGEBRA;

EID: 0027644161     PISSN: 10636706     EISSN: 19410034     Source Type: Journal    
DOI: 10.1109/91.236551     Document Type: Article

References (34)

1. L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems, vol. 1, pp. 3–28, 1978.
2. L. A. Zadeh, “Possibility theory and its application to information analysis,” in Proc. Int. Colloq. Inform. Theory (CNRS, Paris), 1978, pp. 173–182.
3. D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty. New York: Plenum Press: 1988.
4. D. Dubois, “Belief structures, possibility theory and decomposable confidence measures on finite sets,” in Computers and Artificial Intelligence, L. Soumis, Ed. Bratislava: 1986, pp. 403–416.
5. D. Dubois, “Possibility theory: Toward normative foundations,” in Decision, Risk and Rationality, B. Munier, Ed. Dordrecht: Reidel, 1988, pp. 601–614.
6. D. Dubois and H. Prade, “Epistemic entrenchment and possibilistic logic,” Artificial Intelligence, vol. 50, pp. 223–239, 1991.
7. D. Dubois and H. Prade, “An introduction to possibilistic and fuzzy logics,” in Non-Standard Logics for Automated Reasoning, P. Smets, A. Mamdani, D. Dubois, and H. Prade, Eds. London: Academic Press, 1988, pp. 287–314.
8. H. S. Stone, Discrete Mathematical Structures and their Applications. Palo Alto, CA: Science Research Associates, 1973.
9. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338–353, 1965.
10. M. Roubens and P. Vincke, Preference Modeling. Berlin: Springer-Verlag, 1989.
11. D. Dubois and H. Prade, “Fuzzy sets in approximate reasoning—Part I: Inference with possibility distributions,” Fuzzy Sets and Systems, vol. 40, pp. 143–202, 1991.
12. L. A. Zadeh, “A theory of approximate reasoning,” in Machine Intelligence, Vol. 9, J. Hayes, D. Michie, and L. I. Mikulich, Eds. New York: Halstead Press, 1979, pp. 149-194.
13. R. R. Yager, “Approximate reasoning as a basis for rule based expert systems,” IEEE Trans. Syst., Man, Cybern., vol. 14, pp. 636–643, 1984.
14. R. R. Yager, “Deductive approximate reasoning systems,” IEEE Trans. Knowledge and Data Engineering, vol. 3, pp. 399–414, 1991.
15. D. Dubois and H. Prade, “The principle of minimum specificity as a basis for evidential reasoning,” in Uncertainty in Knowledge-Based Systems, B. Bouchon and R. R. Yager, Eds. Berlin: Springer-Verlag, 1987, pp. 75–84.
16. R. R. Yager, “Measuring tranquility and anxiety in decision making: An application of fuzzy sets,” Int. J. General Syst., vol. 8, pp. 139–146, 1982.
17. R. R. Yager, “Entropy and specificity in a mathematical theory of evidence,” Int. J. General Syst., vol. 9, pp. 249–260, 1983.
18. R. R. Yager, “Measuring the quality of linguistic forecasts,” Int. J. Man-Machine Studies, vol. 21, pp. 253–257, 1984.
19. R. R. Yager, “Measures of specificity for possibility distributions,” in Proc. IEEE Workshop on Languages for Automation: Cognitive Aspects in Information Processing (Palma de Mallorca, Spain), 1985, pp. 209–214.
20. R. R. Yager, “Toward a general theory of reasoning with uncertainty—Part I: Nonspecificity and fuzziness,” Int. J. Intell. Syst., vol. 1, pp. 45–67, 1986.
21. R. R. Yager, “Similarity based specificity measures,” Int. J. General Syst., vol. 19, pp. 91–106, 1991.
22. R. R. Yager, “Default knowledge and measures of specificity,” Inform. Sci., vol. 61, pp. 1–14, 1992.
23. M. Higashi and G. J. Klir, “Measures of uncertainty and information based on possibility distributions,” Int. J. General Syst., vol. 9, pp. 43–58, 1983.
24. G. J. Klir and T. A. Folger, Fuzzy Sets, Uncertainty and Information. Englewood Cliffs, NJ: Prentice-Hall, 1988.
25. D. Dubois and H. Prade, “A note on measures of specificity for fuzzy sets,” Int. J. General Syst., vol. 10, pp. 279–283, 1985.
26. A. J. Ramer and R. R. Yager, “Analysis of specificity of fuzzy sets,” in Proc. IEEE-FUZZ Conf. (San Diego), 1992, pp. 1097-1104.
27. A. Ramer and R. R. Yager, “Specificity models of possibilistic uncertainty,” in Proc. Fourth Int. Conf. Information Processing and Management of Uncertainty (Palma de Majorca, Spain), 1992, pp. 95–98.
28. R. R. Yager, “On the completion of priority orderings in nonmonotonic reasoning systems,” Tech. Rep. MII No. 1224, Machine Intelligence Institute, Iona College, New Rochelle, NY, 1992.
29. F. S. Roberts, Measurement Theory. Reading, MA: Addison-Wesley, 1979.
30. R. R. Yager and D. P. Filev, “Parameterized and like and or like OWA operators,” Int. J. General Systems, to be published.
31. D. Filev and R. R. Yager, “A generalized defuzzification method under BAD distributions,” Int. J. Intelligent Syst., vol. 6, pp. 687–697, 1991.
32. A. DeLuca and S. Termini, “A definition of a non-probabilistic entropy in the setting of fuzzy sets,” Information and Control, vol. 20, pp. 301–312, 1972.
33. P. Gardenfors, Knowledge in Flux. Cambrige, MA: MIT Press, 1988.
34. C. L. Shannon, “The mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 623–656, 1948.