Inductive Logic Programming: Theory and Methods

Inductive Logic Programming (ILP) is a new discipline which investigates the inductive construction of rst-order clausal theories from examples and background knowledge. We survey the most important theories and methods of this new eld. Firstly, various problem speciications of ILP are formalised in semantic settings for ILP, yielding a \model-theory" for ILP. Secondly, a generic ILP algorithm is presented. Thirdly, the inference rules and corresponding operators used in ILP are presented, resulting in a \proof-theory" for ILP. Fourthly, since inductive inference does not produce statements which are assured to follow from what is given, inductive inferences require an alternative form of justiication. This can take the form of either probabilistic support or logical constraints on the hypothesis language. Information compression techniques used within ILP are presented within a unifying Bayesian approach to connrmation and corroboration of hypotheses. Also, diierent ways to constrain the hypothesis language, or specify the declarative bias are presented. Fifthly, some advanced topics in ILP are addressed. These include aspects of computational learning theory as applied to ILP, and the issue of predicate invention. Finally, we survey some applications and implementations of ILP. ILP applications fall under two diierent categories: rstly scientiic discovery and knowledge acquisition, and secondly programming assistants.