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 bioinformatics and natural language processing.
References
- S.H. Muggleton. Inductive Logic Programming. New Generation Computing, 8(4):295-318, 1991.
- S.H. Muggleton, Inverse Entailment and Progol, New Generation Computing Journal, Vol. 13, pp. 245-286, 1995.
- S.H. 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, ISBN 0-13-457870-8 Publicly available online version
Implementations
- Progol ( http://www.doc.ic.ac.uk/~shm/Software/ )
- 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 )
- Warmr ( http://www.cs.kuleuven.ac.be/~ml/Doc/TW_User/ )
- RSD ( http://labe.felk.cvut.cz/~zelezny/rsd )