Turning 30: New Ideas in Inductive Logic Programming
Turning 30: New Ideas in Inductive Logic Programming
Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Survey track. Pages 4833-4839.
https://doi.org/10.24963/ijcai.2020/673
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 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.
Keywords:
Machine Learning: general
Knowledge Representation and Reasoning: general