Inductive Logic Programming

Inductive Logic Programming (ILP) is a research area formed at the intersection of Machine Learning and Logic Programming. ILP systems develop predicate descriptions from examples and background knowledge. The examples, background knowledge and final descriptions are all described as logic programs. A unifying theory of Inductive Logic Programming is being built up around lattice-based concepts such as refinement, least general generalisation, inverse resolution and most specific corrections. In addition to a well established tradition of learning-in-the-limit results, some results within Valiant's PAC-learning framework have been demonstrated for ILP systems. U-learnability, a new model of learnability, has also been developed.
Presently successful applications areas for ILP systems include the learning of structure-activity rules for drug design, finite-element mesh analysis design rules, primary-secondary prediction of protein structure and fault diagnosis rules for satellites.