Multiple Predicate Learning in Two Inductive Logic Programming Settings

Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction between two diierent semantics used in inductive logic programming, and illustrates their application in knowledge discovery and programming. Whereas most research in inductive logic programming has focussed on learning single predicates from given datasets using the normal ILP semantics (e.g. the well known ILP systems GOLEM and FOIL), the paper investigates also the non-monotonic ILP semantics and the learning problems involving multiple predicates. The non-monotonic ILP setting avoids the order dependency problem of the normal setting when learning multiple predicates, extends the representation of the induced hypotheses to full clausal logic, and can be applied to diierent types of application.