Abstract

We use the interpretation of fuzzy sets in terms of coherent conditional probabilities for handling probabilistic fuzzy IF-THEN rules. We show by some examples how this interpretation can help when fuzzy and statistical information need to be combined and the available probabilistic information on the fuzzy events is possibly imprecise or incomplete.

Keywords:
Probabilistic logic Fuzzy logic Interpretation (philosophy) Artificial intelligence Fuzzy classification Computer science Fuzzy set Conditional probability Fuzzy set operations Data mining Type-2 fuzzy sets and systems Defuzzification Machine learning Fuzzy number Mathematics Statistics

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
54
Refs
0.10
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Rough Sets and Fuzzy Logic
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Fuzzy Logic and Control Systems
Physical Sciences →  Computer Science →  Artificial Intelligence
Logic, Reasoning, and Knowledge
Physical Sciences →  Computer Science →  Artificial Intelligence

Related Documents

BOOK-CHAPTER

Probabilistic versus Fuzzy Reasoning

Peter Cheeseman

Machine intelligence and pattern recognition Year: 1986 Pages: 85-102
BOOK-CHAPTER

Probabilistic Reasoning in a Fuzzy Context

Giulianella ColettiBarbara Vantaggi

Studies in fuzziness and soft computing Year: 2014 Pages: 97-115
JOURNAL ARTICLE

Reasoning about actions in a probabilistic setting

Chitta BaralNam TranLe-Chi Tuan

Journal:   National Conference on Artificial Intelligence Year: 2002 Pages: 507-512
© 2026 ScienceGate Book Chapters — All rights reserved.