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Issue:Intuitionistic fuzzy Prolog: Difference between revisions

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# G. Gargov and K. Atanassov, [[Issue:Two results in intuitionistic fuzzy logic|Two results in intuitionistic fuzzy logic]], C.R. Acad. Bulgare Sci. (submitted).
# G. Gargov and K. Atanassov, [[Issue:Two results in intuitionistic fuzzy logic|Two results in intuitionistic fuzzy logic]], C.R. Acad. Bulgare Sci. (submitted).
# M.A.S. Guth, Some uses and limitations of fuzzy logic in artificial intelligence reasoning for reactor control, Nuclear Eng. and Design 113 (1989) 990-109.
# M.A.S. Guth, Some uses and limitations of fuzzy logic in artificial intelligence reasoning for reactor control, Nuclear Eng. and Design 113 (1989) 990-109.
# C. Hinde, Fuzzy Proiog, lnternat. J. Man-Machine Systems 24 (1986) 569-595.
# C. Hinde, Fuzzy Prolog, lnternat. J. Man-Machine Systems 24 (1986) 569-595.
# T. Martin, J. Baldwin and B. Polsworth, FRROLOG - A fuzzy Prolog interpreter, ITRC Res. Report 50, Univ. of Bristol (1984).
# T. Martin, J. Baldwin and B. Polsworth, FRROLOG - A fuzzy Prolog interpreter, ITRC Res. Report 50, Univ. of Bristol (1984).
# T. Martin, J. Baldwin and B. Polsworth, The implementation of FRROLOG - A fuzzy Prolog interpreter, Fuzzy Sets and Systems 23 (1987) 119-129.
# T. Martin, J. Baldwin and B. Polsworth, The implementation of FRROLOG - A fuzzy Prolog interpreter, Fuzzy Sets and Systems 23 (1987) 119-129.

Revision as of 12:44, 11 September 2009

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http://ifigenia.org/wiki/issue:fss-53-121-128
Title of paper: Intuitionistic fuzzy Prolog
Author(s):
Krassimir Atanassov
Institute for Microsystems, Sofia, Bulgaria
   (current: krat@bas.bg)
Christo Georgiev
Department of Computer Science, Higher Institute of Mechanical and Electrical Engineering, Varna, Bulgaria
Published in: Fuzzy Sets and Systems, 53 (1993) pp 121-128
Download:  PDF (444  Kb, File info)
Abstract: A logic programming system which uses a theory of intuitionistic fuzzy sets to model various forms of uncertainty is presented. To represent uncertainty of facts and rules, a pair of two different real numbers (degree of truth and degree of falsity) are associated. The problem of propagating uncertainty through logical inference and various models of interpretation are considered. The framework discussed allows knowledge representation and inference under uncerta inty in the form of rules suitable for expert systems.
Keywords: Expert systems, Intuitionistic fuzzy logic, Intuitionistic fuzzy sets, Logic programming.
References:
  1. K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20 (1986) 87-96.
  2. K. Atanassov, Two variants of intuitionistic fuzzy propositional calculus, Preprint IM-MFAIS-5-88, Sofia (1988).
  3. K. Atanassov, Two variants of intuitionistic fuzzy modal logic, Preprint IM-MFAIS-3-89, Sofia, (1989).
  4. K. Atanassov and G. Gargov, Intuitionistic fuzzy logic, C.R. Acad. Bulgare. Sci. 43(3) (1990) 9-12.
  5. K. Atanassov, Two operators on intuitionistic fuzzy sets, C.R. Acad. Bulgare Sci. 41(5) (1988) 35-38.
  6. K. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Sets and Systems 33 (1989) 1-9.
  7. K. Atanassov, Four new operators on intuitionistic fuzzy sets, Preprint IM-MFAIS-4-89, Sofia (1989).
  8. J.F. Baldwin, Support logic programming, in: A. Jones et al., Eds., Fuzzy Sets Theory and Applications (Reidel, Dordrecht-Boston, 1986) 133-170.
  9. D. Dubois and H. Prade, On the combination of evidence in various mathematical frameworks, Rapport L.S.I. No. 312 (Dec. 1988).
  10. D. Dubois and H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Comput. Intell. 4 (1988) 244-264.
  11. G. Gargov and K. Atanassov, Two results in intuitionistic fuzzy logic, C.R. Acad. Bulgare Sci. (submitted).
  12. M.A.S. Guth, Some uses and limitations of fuzzy logic in artificial intelligence reasoning for reactor control, Nuclear Eng. and Design 113 (1989) 990-109.
  13. C. Hinde, Fuzzy Prolog, lnternat. J. Man-Machine Systems 24 (1986) 569-595.
  14. T. Martin, J. Baldwin and B. Polsworth, FRROLOG - A fuzzy Prolog interpreter, ITRC Res. Report 50, Univ. of Bristol (1984).
  15. T. Martin, J. Baldwin and B. Polsworth, The implementation of FRROLOG - A fuzzy Prolog interpreter, Fuzzy Sets and Systems 23 (1987) 119-129.
  16. Y. Sakakibara, Programming in modal logic: an extension of Prolog based on modal logic, Lecture Notes in Computer Science No. 264 (1987) 81-91.
  17. H.-J. Zimmermann, and P. Zysno, Latent connectives in human decision making, Fuzzy Sets and Systems 4 (1980) 37-51.
  18. H.-J. Zimmermann and P. Zysno, Decisions and evaluations by hierarchical aggregation of information, Fuzzy Sets and Systems 10 (1983) 243-260.
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