Abstract Inductive Logic Programming (ILP) is a new discipline which investigates the
inductive construction of first-order clausal theories from examples and background …

This paper summarizes the current state of the art in the topics of rst-order theory revision
and theory restructuring. The various tasks involved in rst-order theory re nement (revision …

The 18th International Conference on Inductive Logic Programming was held in Prague,
September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

FOIL is a learning system that constructs Horn clause programs from examples. This paper
summarises the development of FOIL from 1989 up to early 1993 and evaluates its …

The eld of machine learning was conceived nearly four decades ago with the bold objective
to develop computational methods that would implement various forms of learning, in …

We describe an inductive logic programming (ILP) approach called learning from failures. In
this approach, an ILP system (the learner) decomposes the learning problem into three …

Standard inductive learning requires that training and test instances come from the same
distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test …

A relational instance-based learning algorithm, called Ribl, is motivated and developed in
this paper. We argue that instancebased methods o er solutions to the often unsatisfactory …

Abstract Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task
of automatically improving an existing knowledge base using learning methods is …