Learning first order fuzzy logic rules

Authors:
Henri Prade;Gilles Richard;Mathieu Serrurier
Affiliations:
IRIT, Universite Paul Sabatier, Toulouse, France;IRIT, Universite Paul Sabatier, Toulouse, France;IRIT, Universite Paul Sabatier, Toulouse, France
Venue:
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Year:
2003

Citing 7
Cited 1

An analysis of first-order logics of probability

Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities

Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Foundations of Inductive Logic Programming

Foundations of Inductive Logic Programming
Knowledge Acquisition from Structured Data: Using Determinate Literals to Assist Search

IEEE Expert: Intelligent Systems and Their Applications
Learning Logical Definitions from Relations

Machine Learning
Induction of Decision Trees

Machine Learning
Modeling uncertain and vague knowledge in possibility and evidence theories

UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence

Learning fuzzy rules with their implication operators

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents an algorithm based on Inductive Logic Programming for inducing first order Horn clauses involving fuzzy predicates from a database. For this, a probabilistic processing of fuzzy function is used, in agreement with the handling of probabilities in first order logic. This technique is illustrated on an experimental application. The interest of learning fuzzy first order logic expressions is emphasized.