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Inductive logic programming (ILP) is a machine learning approach, which uses techniques of logic programming. From a database of facts and expected results, which are divided into positive and negative examples, an ILP system tries to derive a logic program that proves all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge = rules.

Inductive logic programming is particularly useful in natural language processing.

1 References

S. Muggleton, Inverse Entailment and Progol, New Generation Computing Journal, Vol. 13, pp. 245-286, 1995.
S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20:629-679, 1994.
N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994, BooksEnthsiast.com Publicly available online version

2 Implementations

Aleph ( http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph )
Foil ( ftp://ftp.cs.su.oz.au/pub/foil6.sh )
Lime ( http://cs.anu.edu.au/people/Eric.McCreath/lime.html )
ACE ( http://www.cs.kuleuven.ac.be/~dtai/ACE/ )
DMax ( http://www.pharmadm.com/dmax.asp )

Machine learning Logic programming

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Topics: Inductive Logic Programming, Inductive Coupling...

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