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 …

An increasing body of work has recognized the importance of exploiting machine learning
(ML) advancements to address the need for efficient automation in extracting access control …

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of
examples in the context of some pre-existing background knowledge. Until recently, most …

Abstract Neuro-Symbolic AI (NeSy) holds promise to ensure the safe deployment of AI
systems, as interpretable symbolic techniques provide formal behaviour guarantees. The …

Given a set of candidate Datalog rules, the Datalog synthesis-as-rule-selection problem
chooses a subset of these rules that satisfies a specification (such as an input-output …

Techniques for learning logic programs from data typically rely on language bias
mechanisms to restrict the hypothesis space. These methods are therefore limited by the …

Recently, novel ILP systems under the answer set semantics have been proposed, some of
which are robust to noise and scalable over large hypothesis spaces. One such system is …