Many inductive logic programming approaches struggle to learn programs f...
Discovering novel abstractions is important for human-level AI. We intro...
The ability to generalise from a small number of examples is a fundament...
Program synthesis approaches struggle to learn programs with numerical
v...
Inductive logic programming is a form of machine learning based on
mathe...
A magic value in a program is a constant symbol that is essential for th...
The goal of inductive logic programming is to induce a set of rules (a l...
The goal of inductive logic programming (ILP) is to search for a hypothe...
We introduce an inductive logic programming approach that combines class...
Multi-core machines are ubiquitous. However, most inductive logic progra...
Discovering novel high-level concepts is one of the most important steps...
Inductive logic programming (ILP) is a form of logic-based machine learn...
Scientists form hypotheses and experimentally test them. If a hypothesis...
Inductive logic programming (ILP) is a form of machine learning. The goa...
We introduce learning programs by learning from failures. In this approa...
Humans constantly restructure knowledge to use it more efficiently. Our ...
A major challenge in inductive logic programming (ILP) is learning large...
Common criticisms of state-of-the-art machine learning include poor
gene...
Most program induction approaches require predefined, often hand-enginee...
A key feature of inductive logic programming (ILP) is its ability to lea...
Many forms of inductive logic programming (ILP) use metarules,
second-or...
General game playing (GGP) is a framework for evaluating an agent's gene...
Children learn though play. We introduce the analogous idea of learning
...
We present the derivation reduction problem for SLD-resolution, the
unde...