Implicative and conjunctive fuzzy rules—a tool for reasoning from knowledge and examples

Authors:
Laurent Ughetto;Didier Dubois;Henri Prade
Affiliations:
-;-;-
Venue:
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Year:
1999

Citing 2
Cited 7

What are fuzzy rules and how to use them

Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Checking the coherence and redundancy of fuzzy knowledge bases

IEEE Transactions on Fuzzy Systems

"Not Impossible" vs. "Guaranteed Possible" in Fusion and Revision

ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Abductive reasoning and measures of similitude in the presence of fuzzy rules

Fuzzy Sets and Systems - Special issue: Preference modelling and applications
Fuzzy inference based on fuzzy concept lattice

Fuzzy Sets and Systems
An overview of the asymmetric bipolar representation of positive and negative information in possibility theory

Fuzzy Sets and Systems
Practical inference with systems of gradual implicative rules

IEEE Transactions on Fuzzy Systems
Bipolar queries: An aggregation operator focused perspective

Fuzzy Sets and Systems
A bipolar possibilistic representation of knowledge and preferences and its applications

WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fuzzy rule-based systems have been mainly used as a convenient tool for synthesizing control laws from data. Recently, in a knowledge representation-oriented perspective, a typology of fuzzy rules has been laid bare, by emphasizing the distinction between implicative and conjunctive fuzzy rules. The former describe pieces of generic knowledge either tainted with uncertainty or tolerant to similarity, while the latter encode examples-originated information expressing either mere possibilities or how typical situations can be extrapolated.The different types of fuzzy rules are first contrasted, and their representation discussed in the framework of possibility theory. Then, the paper studies the conjoint use of fuzzy rules expressing knowledge (as fuzzy constraints which restrict the possible states of the world), or gathering examples (which testify the possibility of appearance of some states). Coherence and inference issues are briefly addressed.