7 citations · 19 across the 17 of their papers we have counts for
4 papers · 1 filter
Inductive logic programming at 30: a new introduction
Andrew Cropper, Sebastijan Dumančić
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…
Learning programs by learning from failures
Andrew Cropper, Rolf Morel
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 se…
Knowledge Refactoring for Inductive Program Synthesis
Sebastijan Dumancic, Tias Guns, Andrew Cropper
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introd…
Turning 30: New Ideas in Inductive Logic Programming
Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
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…