Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling

1999 ◽  
pp. 95-117 ◽  
Author(s):  
O. Cordón ◽  
A. González ◽  
F. Herrera ◽  
R. Pérez
Keyword(s):  
Rule Learning ◽  
Learning Approach ◽  
Qualitative Modeling

scholarly journals Identifying users of traditional and Internet-based resources for meal ideas: An association rule learning approach

Appetite ◽  
2016 ◽  
Vol 103 ◽  
pp. 128-136 ◽  
Author(s):  
Allison E. Doub ◽  
Meg L. Small ◽  
Aron Levin ◽  
Kristie LeVangie ◽  
Timothy R. Brick
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Learning Approach

LEARNING LOGIC RULES FOR THE TOWER OF KNOWLEDGE USING MARKOV LOGIC NETWORKS

2011 ◽  
Vol 25 (06) ◽  
pp. 889-907 ◽  
Author(s):  
MAI XU ◽  
MARIA PETROU ◽  
JIANHUA LU
Keyword(s):  
Rule Learning ◽  
Point Of View ◽  
Learning Approach ◽  
Markov Logic Networks ◽  
Learning Capability ◽  
Markov Logic ◽  
First Order ◽  
Scene Interpretation ◽  
The Right ◽  
Logic Rule

In this paper, we propose a novel logic-rule learning approach for the Tower of Knowledge (ToK) architecture, based on Markov logic networks, for scene interpretation. This approach is in the spirit of the recently proposed Markov logic networks for machine learning. Its purpose is to learn the soft-constraint logic rules for labeling the components of a scene. In our approach, FOIL (First Order Inductive Learner) is applied to learn the logic rules for MLN and then gradient ascent search is utilized to compute weights attached to each rule for softening the rules. This approach also benefits from the architecture of ToK, in reasoning whether a component in a scene has the right characteristics in order to fulfil the functions a label implies, from the logic point of view. One significant advantage of the proposed approach, rather than the previous versions of ToK, is its automatic logic learning capability such that the manual insertion of logic rules is not necessary. Experiments of labeling the identified components in buildings, for building scene interpretation, illustrate the promise of this approach.

scholarly journals An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach

2015 ◽  
Vol 67 ◽  
pp. 37-58 ◽  
Author(s):  
David García ◽  
Juan Carlos Gámez ◽  
Antonio González ◽  
Raúl Pérez
Keyword(s):  
Rule Learning ◽  
Fuzzy Rule ◽  
Learning Approach ◽  
Rule Bases
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