ILP turns 20 Biography and future challenges

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characteris...

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Bibliographic Details
Published inMachine learning Vol. 86; no. 1; pp. 3 - 23
Main Authors Muggleton, Stephen, De Raedt, Luc, Poole, David, Bratko, Ivan, Flach, Peter, Inoue, Katsumi, Srinivasan, Ashwin
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.01.2012
Subjects
Age
Analogies
Artificial Intelligence
Biographies
Computer Science
Control
Human
Logic programming
Machine learning
Mechatronics
Natural Language Processing (NLP)
Projection
Recall
Robotics
Simulation and Modeling
Structured data in Machine Learning
Inductive Logic Programming
(Statistical) relational learning
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Summary:Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-011-5259-2