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 …

Many recent works have proposed methods to train classifiers with local robustness
properties, which can provably eliminate classes of evasion attacks for most inputs, but not …

Graph Neural Networks (GNNs) are often used to realise learnable transformations of graph
data. While effective in practice, GNNs make predictions via numeric manipulations in an …

Satisfiability modulo theories (SMT) solving has become a critical part of many static
analyses, including symbolic execution, refinement type checking, and model checking. We …

Despite achieving superior performance in human-level control problems, unlike humans,
deep reinforcement learning (DRL) lacks high-order intelligence (eg, logic deduction and …

Common data types like dates, addresses, phone numbers and tables can have multiple
textual representations, and many heavily-used languages, such as SQL, come in several …

Programming by example (PBE) is an emerging programming paradigm that automatically
synthesizes programs specified by user-provided input-output examples. Despite the …