Turning 30: New Ideas in Inductive Logic Programming
This is an incremental survey paper that reviews progress in ILP for researchers interested in interpretable and data-efficient machine learning.
The paper surveys recent advances in inductive logic programming (ILP) that address common machine learning limitations like poor generalization and lack of interpretability, highlighting methods for learning recursive programs and background knowledge from few examples.
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), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.