International Journal of Intelligent Systems

Prosum-Prolog system for uncertainty management

Corresponding Author

Ion Iancu

University of Craiova, Department of Mathematics and Computer Science, 13 A. I. Cuza Street, 1100, Craiova, Romania

University of Craiova, Department of Mathematics and Computer Science, 13 A. I. Cuza Street, 1100, Craiova, RomaniaSearch for more papers by this author

Ion Iancu,

Corresponding Author

Ion Iancu

University of Craiova, Department of Mathematics and Computer Science, 13 A. I. Cuza Street, 1100, Craiova, Romania

University of Craiova, Department of Mathematics and Computer Science, 13 A. I. Cuza Street, 1100, Craiova, RomaniaSearch for more papers by this author

First published: 07 December 1998

https://doi.org/10.1002/(SICI)1098-111X(199709)12:9<615::AID-INT1>3.0.CO;2-M

Citations: 1

About

PDF

Tools

Share a link

Email
Wechat
Bluesky

Abstract

In this paper we present a system which uses knowledge represented in the form of production rules accompanied by uncertainty degrees. The uncertainty of a rule is given by using the method from SLOP and FRIL: a support pair, which comprises a necessary and possible support and can be interpreted as an interval in which the unknown probability lies. Giving the knowledge in this form, our system generates a Turbo Prolog program which has included the operations for uncertainty management. In this version, two kinds of rules for support logic programming are implemented but the user can propose the other ones, by using the dialogue with the system. Semantic unification differs from that used in FRIL; we use generalized belief functions. © 1997 John Wiley & Sons, Inc.

References

1 J. F. Baldwin “Evidential support logic programming,” Fuzzy Sets and Systems, 24, 1–26 (1987).
10.1016/0165-0114(87)90110-2
Web of Science® Google Scholar
2 J. F. Baldwin and M. R. M. Monk, “ SLOP–a system for support logic programming,” I.T.R.C. Research Report, Univ. of Bristol (1986).
Google Scholar
3 J. F. Baldwin, B. W. Pilsworth, and T. P. Martin, “ FRIL: A Support Logic Programming System, version 4.0, Programmer's Manual,” I.T.R.C., Univ of Bristol.
Google Scholar
4 J. F. Baldwin and B. W. Pilsworth “Semantic unification with fuzzy concepts in FRIL,” Int. J. of Intell. Systems, 7, 61–69 (1992).
10.1002/int.4550070108
Web of Science® Google Scholar
5 R. Martin-Clouaire and H. Prade “ SPII-A Simple Inference Engine Capable of Accommodating Both Imprecision and Uncertainty,” Rapport L.S.I.n° 247, 1986, pp. 92–106.
Google Scholar
6 J. Lebailly, R. Martin-Clouaire, and H. Prade, “ Use of fuzzy logic in a rule-based system in petroleum geology,” Approximate Reasoning in Intelligent Systems, E. Sanchez and L. A. Zadeh, Eds., Pergamon Press, New York, 1987, pp. 125–144.
Google Scholar
7 J. C. Buisson, H. Farreny, and H. Prade, “ Dealing with impression and uncertainty in the expert system DIABETO III,” Proceedings of the 2nd Int. Conf. on Artificial Intelligence, Marseille (1986), pp. 705–721.
Google Scholar
8 J. C. Buisson, “ TOULMED, un generateur de systemes experts qui prend en compte ľimprecision et ľincertitude des connaissances,” These, Institut National Polytechnique de Toulouse, 1987.
Google Scholar
9 E. Wyss, “ TAIGER, un generateur de systemes experts adapte au traitement de donnees incertaines et imprecises,” These, Institut National Polytechnique de Toulouse, 1988.
Google Scholar
10 K. S. Leung and W. Lam “A fuzzy expert system shell using both exact and inexact reasoning,” J. of Automated Reasoning, 5, 207–233 (1989).
10.1007/BF00243003
Google Scholar
11 A. M. Whiter, “ PFL: Pi-fuzzy logic. A practical fuzzy logic,” in Shortdeskription Systems Des. Ltd., Hampshire (1983).
Google Scholar
12 J. F. Baldwin and B. W. Pilsworth “Fuzzy truth definition of possibility measure for decision classification,” Int. J. Man-Machine Studies, 2, 447–463 (1979).
10.1016/S0020-7373(79)80037-1
Web of Science® Google Scholar
13 Q. Zhu, E. S. Lee “Dempster-Shafer Approach in Propositional Logic,” Int. J. Intelli. Systems, 8, 341–349 (1993).
10.1002/int.4550080302
Web of Science® Google Scholar
14 J. Yen “Computing generalized belief functions for continuous fuzzy sets,” Int. J. Approxi. Reasoning, 6, 1–31 (1992).
10.1016/0888-613X(92)90037-Z
Google Scholar
15 G. Shafer, Mathematical Theory of Evidence, Princeton University Press, Princeton, N.J. 1976.
10.1515/9780691214696
Google Scholar
16 P. Smets “The degree of belief in a fuzzy event,” Information Sci., 25, 1–19 (1981).
10.1016/0020-0255(81)90008-6
Web of Science® Google Scholar
17 L. A. Zadeh “The concept of a linguistic variable and its application to approximate reasoning,” Information Sci., 8, 199–249 (1975).
10.1016/0020-0255(75)90036-5
Web of Science® Google Scholar
18 D. Dubois and H. Prade, “ On several representations of an uncertain body of evidence,” Fuzzy Information and Decision Processes, M. M. Gupta and E. Sanchez, Eds., North-Holland, Amsterdam, 1982, pp. 167–181.
Google Scholar
19 D. Dubois and H. Prade “Evidence measures based on fuzzy information,” Automatica, 21(5), 547–562 (1985).
10.1016/0005-1098(85)90004-4
Web of Science® Google Scholar

Citing Literature

All articles

Prosum-Prolog system for uncertainty management

Abstract

References

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Prosum-Prolog system for uncertainty management

Abstract

References

Citing Literature

References

Related

Information