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Challenges for Inductive Logic Programming
1999Inductive logic programming (ILP) is a research area that has its roots in inductive machine learning and logic programming. Computational logic has significantly influenced machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge.
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Inductive logic programming beyond logical implication
1996This paper discusses the generalization of definite Horn programs beyond the ordering of logical implication. Since the seminal paper on generalization of clauses based on θ subsumption, there are various extensions in this area. Especially in inductive logic programming(ILP), people are using various methods that approximate logical implication, such ...
Jianguo Lu, Jianguo Lu, Jun Arima
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A Perspective on Inductive Logic Programming
1999The state-of-the-art in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, 2) theory, techniques and applications, and 3) various technical problems addressed within inductive logic programming.
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Approaches to inductive logic programming
1992Inductive Logic Programming (ILP) is concerned with construction of logic programs from examples. It shares many concerns of Machine Learning (ML), but is committed to logic. As logic can help to provide a basis for elaborating such a methodology for learning, the area of ILP has attracted a wide attention of many researchers.
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Inductive logic programming and constraint logic programming (abstract)
1997openaire +2 more sources