GR2: a hybrid knowledge-based system using general rules

Authors:
Zhe Ma;Robert F. Harrison;R. Lee Kennedy
Affiliations:
The University of Sheffield, Department of Automatic Control and Systems Engineering, Sheffield, UK;The University of Sheffield, Department of Automatic Control and Systems Engineering, Sheffield, UK;Department of Medicine, The University of Edinburgh, UK
Venue:
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Year:
1995

Citing 4
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks

Machine Learning
Neural Networks in Computer Intelligence

Neural Networks in Computer Intelligence
Using symbolic learning to improve knowledge-based neural networks

AAAI'92 Proceedings of the tenth national conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

GR2 is a hybrid knowledge-based system consisting of a Multilayer Perceptron (MLP) and a rule-based system for hybrid knowledge representations and reasoning. Knowledge embedded in the trained MLP is extracted in the form of general (production) rules--a natural format of abstract knowledge representation. The rule extraction method integrates Black-box and Open-box techniques, obtaining feature salient and statistical properties of the training pattern set. The extracted general rules are quantified and selected in a rule validation process. Multiple inference facilities such as categorical reasoning, probabilistic reasoning and exceptional reasoning are performed in GR2.