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 in a paper by Stephen Muggleton in 1991.
Read more about Inductive Logic Programming: Implementations
Famous quotes containing the words logic and/or programming:
“Neither Aristotelian nor Russellian rules give the exact logic of any expression of ordinary language; for ordinary language has no exact logic.”
—Sir Peter Frederick Strawson (b. 1919)
“If there is a price to pay for the privilege of spending the early years of child rearing in the driver’s seat, it is our reluctance, our inability, to tolerate being demoted to the backseat. Spurred by our success in programming our children during the preschool years, we may find it difficult to forgo in later states the level of control that once afforded us so much satisfaction.”
—Melinda M. Marshall (20th century)