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 …

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to
induce a hypothesis (a logic program) that generalises given training examples and …

Abstract Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty as a belief …

The goal of inductive logic programming is to induce a logic program (a set of logical rules)
that generalises training examples. Inducing programs with many rules and literals is a …

The ability to generalise from a small number of examples is a fundamental challenge in
machine learning. To tackle this challenge, we introduce an inductive logic programming …

A magic value in a program is a constant symbol that is essential for the execution of the
program but has no clear explanation for its choice. Learning programs with magic values is …

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises
training examples and background knowledge (BK). To improve performance, we introduce …

Discovering novel high-level concepts is one of the most important steps needed for human-
level AI. In inductive logic programming (ILP), discovering novel high-level concepts is …

The predicates used for Inductive Logic Programming (ILP) systems are usually elusive and
need to be hand-crafted in advance, which limits the generalization of the system when …

Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a
machine learning system similar abilities so that it can learn more efficiently. We introduce …