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Inductive logic programming

Inductive logic programming (ILP) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge => hypothesis.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. The term Inductive Logic Programming was first introduced[1] in a paper by Stephen Muggleton in 1991.


Implementations

See also

References

  • Muggleton, S. (1991). "Inductive logic programming". New Generation Computing 8 (4): 295–318. doi:10.1007/BF03037089. 
  • Muggleton, S. (1994). "Inductive Logic Programming: Theory and methods". The Journal of Logic Programming 19-20: 629–679. doi:10.1016/0743-1066(94)90035-3. 
  • 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.
  1. ^ Luc De Raedt. A Perspective on Inductive Logic Programming. The Workshop on Current and Future Trends in Logic Programming, Shakertown, to appear in Springer LNCS, 1999. doi:10.1.1.56.1790

cz:Induktivní logické programování

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