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 …

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of
interpretability, and a need for large amounts of training data. We survey recent work 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 …

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 …

Learning complex programs through inductive logic programming (ILP) remains a
formidable challenge. Existing higher-order enabled ILP systems show improved accuracy …

Understanding the interactions of agents trained with deep reinforcement learning is crucial
for deploying agents in games or the real world. In the former, unreasonable actions confuse …

Learning first-order logic programs from relational facts yields intuitive insights into the data.
Inductive logic programming (ILP) models are effective in learning first-order logic programs …

Transparency is a key requirement for ethical machines. Verified ethical behavior is not
enough to establish justified trust in autonomous intelligent agents: it needs to be supported …

Despite tremendous progress, machine learning and deep learning still suffer from
incomprehensible predictions. Incomprehensibility, however, is not an option for the …

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 …