Inductive logic programming

Inductive logic programming (ILP) is a subfield of <a href="/facts/Symbolic_artificial_intelligence/ACBwbcG6">symbolic artificial intelligence</a> which uses <a href="/facts/Logic_programming/Xgh8lj3F">logic programming</a> as a uniform representation for examples, background knowledge and hypotheses. The term "inductive" here refers to <a href="/facts/Inductive_reasoning/EJ992JJ9">philosophical</a> (i.e. suggesting a theory to explain observed facts) rather than <a href="/facts/Mathematical_induction/KxAPqNrH">mathematical</a> (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 <a href="/facts/Database/VYcpkevb">database</a> of facts, an ILP system will derive a hypothesised logic program which <a href="/facts/Entailment/AyDXp4dn">entails</a> all the positive and none of the negative examples.

<ul><li>Schema: positive examples + negative examples + background knowledge ⇒ hypothesis.</li></ul>
Inductive logic programming is particularly useful in <a href="/facts/Bioinformatics/D5x2L8ee">bioinformatics</a> and <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a>.

Inductive logic programming open-in-new

Inductive logic programming