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 …

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 …

We propose a new approach to synthesize Datalog programs from input-output
specifications. Our approach leverages query provenance to scale the counterexample …

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 …

The problem of learning logical rules from examples arises in diverse fields, including
program synthesis, logic programming, and machine learning. Existing approaches either …

Template-based synthesis, also known as sketching, is a localized approach to program
synthesis in which the programmer provides not only a specification, but also a high-level" …

This paper describes a deductive approach to synthesizing imperative programs with
pointers from declarative specifications expressed in Separation Logic. Our synthesis …

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

Many programs that interact with a database need to undergo schema refactoring several
times during their life cycle. Since this process typically requires making significant changes …