Scientists aim to discover meaningful formulae that accurately describe experimental data.
Mathematical models of natural phenomena can be manually created from domain …

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 …

Abstract We propose Neuro-Symbolic Hierarchical Rule Induction, an efficient interpretable
neuro-symbolic model, to solve Inductive Logic Programming (ILP) problems. In this model …

Abstract Inductive Logic Programming (ILP) systems aim to find a set of logical rules, called
a hypothesis, that explain a set of examples. In cases where many such hypotheses exist …

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 …

The integration of reasoning, learning, and decision-making is key to build more general
artificial intelligence systems. As a step in this direction, we propose a novel neural-logic …

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

The ability to efficiently solve hard combinatorial optimization problems is a key prerequisite
to various applications of declarative programming paradigms. Symmetries in solution …