Inductive logic programming: Difference between revisions

Revision as of 04:56, 21 June 2007

Revision as of 05:18, 21 June 2007

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'''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 deriveswill derive an hypothesised logic program which entails all the positive and none of the negative examples.

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