A new perspective on reasoning with fuzzy rules

International Journal of Intelligent Systems

Volumn 18, Issue 5, 2003, Pages 541-567

  Dubois, D ^a   Prade, H ^a   Ughetto, L ^b  

^a IRIT   (France)

^b UNIVERSITÉ DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; FUZZY SETS; INTERPOLATION; KNOWLEDGE REPRESENTATION; MANY VALUED LOGICS; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS; RELATIONAL DATABASE SYSTEMS;

FUZZY INFERENCE; FUZZY RULES;

KNOWLEDGE BASED SYSTEMS;

EID: 0037716845     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.10103     Document Type: Article

References (51)

1. Adams, EW. The logic of conditionals. Dordrecht, The Netherlands: D. Reidel; 1975.
2. Baldwin JF, Guild NCF. Modelling controllers using fuzzy relations. Kybernetes 1980;9:223-229.
3. Borgelt C, Kruse R. Graphical models-methods for data analysis and mining. Chichester, UK: J. Wiley & Sons; 2001.
4. Borgelt C, Kruse R. Learning from imprecise data: Possibilistic graphical models. Computat Stat Data Anal 2002;38:449-463.
5. Bouchon-Meunier B, Delechamp J, Marsala C, Mellouli N, Rifqi M, Zerrouki L. Analogy and fuzzy interpolation in case of sparse rules. In: Proc EUROFUSE-SIC Joint Conference. Budapest, Hungary; 1999. pp 132-136.
6. Bouchon-Meunier B, Dubois D, Godo L, Prade H. Fuzzy sets and possibility theory in approximate and plausible reasoning. In: Bezdek J, Dubois D, Prade H, editors. Fuzzy sets in approximate reasoning and information systems, The handbooks of fuzzy sets. Boston: Kluwer; 1999, pp 15-190.
7. Bouchon-Meunier B, Dubois D, Marsala C, Prade H, Ughetto L. A comparative view of interpolation methods between sparse fuzzy rules. In: Proc Joint 9th IFSA World Congress and 20th NAFIPS Int Conf, Vol 5, Vancouver, Canada: New York: IEEE Press; 2001, pp. 2499-2504.
8. Bouchon-Meunier B, Marsala C, Rifqi M. Interpolative reasoning based on graduality. In: Proc 9th IEEE Int Conf on Fuzzy Systems (FUZZ-IEEE'2000). San Antonio, TX; 2000. pp. 483-487.
9. Calabrese, P. An algebraic synthesis of the foundations of logic and probability. Inf Sci 1987;42:187-237.
10. De Baets B, Kerre E. Fuzzy relational compositions. Fuzzy Sets Syst 1993;60:109-120.
11. De Finetti B. La logique des probabilités. In: Actes du Congrès International de Philosophie Scientifique, Vol 4, Paris, France. Paris, France: Hermann et Cie; 1936. pp 1-9.
12. De Finetti B. Theory of probability-a critical introductory treatment, Vol 1 and 2. Chichester, UK: John Wiley & Sons; 1974.
13. Di Nola A, Pedrycz W, Sessa S. An aspect of discrepancy in the implementation of modus ponens in the presence of fuzzy quantities. Int J Approx Reason 1989;3:259-265.
14. Dubois D, Grabisch M, Prade H. Gradual rules and the approximation of control laws. In: Nguyen HT, Sugeno M, Tong R, Yager RR, editors. Theoretical aspects of fuzzy control. New York: Wiley; 1994, pp. 147-181.
15. Dubois D, Hajek, P, Prade H. Knowledge driven vs. data driven logics. J Logic Language Inf 2000;9:65-89.
16. Dubois D, Hüllermeier E, Prade H. Flexible control of case based prediction in the framework of possibility theory. In: Proc 5th European Workshop on Case-Based Reasoning (EWCBR'00), LNCS, 1898. Berlin: Springer Verlag; 2000. pp 61-73.
17. Dubois D, Martin-Clouaire R, Prade H. Practical computing in fuzzy logic. In: Gupta MM, Yamakawa T, editors. Fuzzy computing. Amsterdam, Holland: North-Holland; 1988. pp. 11-34.
18. Dubois D, Prade H. A typology of fuzzy "if...,then..." rules. In: Proc 3rd Int Fuzzy Systems Association Congress (IFSA'89). Seattle, WA; August 1989. pp 782-785.
19. Dubois D, Prade H. Fuzzy rules in knowledge-based systems-modeling gradedness, uncertainty and preference. In: Yager RR, Zadeh LA, editors. An introduction to fuzzy logic applications in intelligent systems. Boston: Kluwer Academic Publishers; 1992. pp 45-68.
20. Dubois D, Prade H. Possibility theory as a basis for preference propagation in automated reasoning. In: Proc 1st IEEE Int Conf on Fuzzy Systems (FUZZ-IEEE'92). San Diego, CA; 1992. pp 821-832.
21. Dubois D, Prade H. Upper and lower images of a fuzzy set induced by a fuzzy relation-applications to fuzzy inference and diagnosis. Inf Sci 1992;64:109-120.
22. Dubois D, Prade H. Conditional objects as non-monotonic consequence relationships. IEEE Trans Syst Man Cybern 1994;24:1724-1739.
23. Dubois D, Prade H. What are fuzzy rules and how to use them. Fuzzy Sets Syst 1996;84:169-186. Special issue in memory of Prof. A. Kaufmann.
24. Dubois D, Prade H. Soft computing, fuzzy logic, and artificial intelligence. Soft Computing 1998;2:7-11.
25. Dubois D, Prade H. On fuzzy interpolation. Int J Gen Syst 1999;28:103-114.
26. Dubois D, Prade H, Ughetto L. Checking the coherence and redundancy of fuzzy knowledge bases. IEEE Trans Fuzzy Syst 1997;5(3):398-417.
27. Dubois D, Prade H, Ughetto L. Fuzzy logic, control engineering and artificial intelligence. In: Verbruggen HB, Zimmermann H-J, Babuska R, editors. Fuzzy algorithms for control engineering. Dordrecht, The Netherlands: Kluwer Academic Publishers; 1999. pp 17-57.
28. Gebhardt J. Learning from data: possibilistic graphical models. In: Abductive reasoning and uncertainty management systems, Vol 4 of Handbook of defeasible reasoning and uncertainty management systems (DRUMS). Kluwer Academic Publishers; 2000, pp 315-389.
29. Goodman IR, Nguyen HT, Walker E. Conditional inference and logic for intelligent systems: a theory of measure-free conditionals. Amsterdam, The Netherlands: North-Holland; 1991.
30. Gyenesei A. A fuzzy approach for mining quantitative association rules. Acta Cybern 2001;15:306-320.
31. Harper WL, Stalnaker R, Pearce G, editors. Ifs. Dordrecht, The Netherlands: D. Reidel; 1981.
32. Hempel CG. A purely syntactic definition of confirmation. J Symb Log 1943;8(4):121-143.
33. Huellermeier E. Fuzzy association rules semantic issues and quality measures. In: Reusch B, editor. Computational intelligence theory and applications, LNCS 2206. Berlin, Germany: Springer Verlag; 2001, pp 380-391.
34. Jenei S. A new approach for interpolation and extrapolation of compact fuzzy quantities. In: Proc 21th Linz Seminar on Fuzzy Set Theory. Linz, Austria; 2000. pp 13-18.
35. Kóczy LT, Hirota K. Approximate reasoning by linear rule interpolation and general approximation. Int J Approx Reason 1993;9:197-225.
36. Kóczy LT, Hirota K. Interpolative reasoning with insufficient evidence in sparse fuzzy rule bases. Inf Sci 1993;71:169-201.
37. Kraus K, Lehmann D, Magidor M. Nonmonotonic reasoning, preferential models and cumulative logics. Artif Intell 1990;44:167-207.
38. Lewis D. Probabilities of conditionals and conditional probabilities. J Philos Log 1973;3, Reprinted in Harper et al.
39. Mamdani EH. Application of fuzzy logic to approximate reasoning using linguistic systems. IEEE Trans Comput 1977;26:1182-1191.
40. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 1975;7:1-13.
41. Mendel J. Fuzzy logic systems for engineering: A tutorial. Proc-IEEE Special Issue Fuzzy Logic Eng Appl 1995;83(3):345-377.
42. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1985;15:116-132.
43. Ughetto L. Inferential independence of fuzzy rules. In: Proc 13th European Conf on Artificial Intelligence (ECAI'98). Brighton, UK: John Wiley & Sons; 1998. pp 605-609.
44. Ughetto L, Dubois D, Prade H. Implicative and conjunctive fuzzy rules-a tool for reasoning from knowledge and examples. In: Proc 16th American Natl Conf on Artificial Intelligence (AAAI'99). Orlando, FL; 1999. pp 214-219.
45. Ughetto L, Dubois D, Prade H. Fuzzy interpolation by convex completion of sparse rule bases. In: Proc 9th IEEE Int Conf on Fuzzy Systems (FUZZ-IEEE'2000). San Antonio, TX; 2000. pp 465-470.
46. Ughetto L, Dubois D, Prade H. Interpolation linéaire par ajout de règles dans une base incomplète. In: Proc des Rencontres Francophones sur la Logique Floue et ses Applications (LFA'00). La Rochelle, France: Cépaduès, Toulouse; 2000. pp 71-78.
47. Weisbrod J. A new approach to fuzzy reasoning. Soft Comput 1998;2:89-99.
48. Zadeh LA. Outline of a new approach to the analysis of complex system and decision processes. IEEE Trans Syst Man Cybern 1973;3:28-44.
49. Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1978;1:3-28.
50. Zadeh LA. A theory of approximate reasoning. In: Mitchie D, Hayes JE, Mikulich LI, editors. Machine intelligence. New York: Elsevier; 1979. pp 149-194.
51. Zadeh LA. The calculus of fuzzy if-then rules. AI Exp 1992;7(3):23-27.