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.
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, ISBN 0-13-457870-8 Publicly available online version (http://www-ai.ijs.si/SasoDzeroski/ILPBook/)
Traditionally, logic is studied as a branch of philosophy Philosophy is a discipline or field of study involving the investigation, analysis, and development of ideas at a general, abstract, or fundamental level.
Intuitionistic logic was proposed by L. Brouwer as the correct logic for reasoning about mathematics, based upon his rejection of the law of the excluded middle as part of his intuitionism.
Again, relevance logic and dialetheism are the most important approaches here, though the concerns are different: the key issue that classical logic and some of its rivals, such as intuitionistic logic have is that they respect the principle of explosion, which means that the logic collapses if it is capable of deriving a contradiction.
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