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Inductive logic programming
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[[File:ILP family2.png|thumb|A photo of Family sample for Inductive Logic Programming article]] '''Inductive logic programming''' ('''ILP''') is a subfield of [[symbolic artificial intelligence]] which uses [[logic programming]] as a uniform representation for examples, background knowledge and hypotheses. The term "''inductive''" here refers to [[Inductive reasoning|philosophical]] (i.e. suggesting a theory to explain observed facts) rather than [[mathematical induction|mathematical]] (i.e. proving a property for all members of a well-ordered set) induction. 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 [[Entailment|entails]] all the positive and none of the negative examples. * Schema: ''positive examples'' + ''negative examples'' + ''background knowledge'' β ''hypothesis''. Inductive logic programming is particularly useful in [[bioinformatics]] and [[natural language processing]].
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