Inductive Logic Programming - Andrew Cropper

Andrew Cropper, logic luminary and creator of the popular Popper, discusses the paper "Inductive Logic Programming at 30: A New Introduction."This episode examines how inductive logic programming (ILP) learns symbolic rules from examples and background knowledge, and what it takes to build ILP systems that scale. As machine learning has shifted toward opaque, data-hungry models, ILP offers a path to interpretable, constrained programs learned from data. The paper distills 30 years of ideas (learning settings, bias, search, recursion, predicate invention, and system design) into a modern entry point for symbolic generalization.Cropper reflects on how the paper emerged alongside his work on Popper, a high-performance ILP system designed around falsification and solver-backed search. He traces this line of thinking back to his training under Stephen Muggleton, the most influential researcher in ILP.In This Episode -

• Inductive bias to constrain search.• Utilizing SAT/ASP-style engines as solver tools.• Why recursion is a decisive capability for true generalization on algorithmic tasks.• Predicate invention enabling more compact programs and better abstraction.• Popper’s core idea: learning by ruling out hypotheses via failures.• A practical research workflow advantage: adding constraints to prune search can yield orders-of-magnitude speedups without rewriting the learner.• ILP in the wild: scientific discovery loops (the "Robot Scientist" pattern), program-by-example tools (Flash Fill), and rule learning to guide RL agents.References -

• https://arxiv.org/abs/2008.07912• https://github.com/logic-and-learning-lab/Popper/• https://www.cs.cmu.edu/~tom/mlbook.html• https://europepmc.org/abstract/MED/14724639• https://www.microsoft.com/en-us/research/publication/automating-string-processing-spreadsheets-using-input-output-examples/About the Paper -

"Inductive logic programming at 30: a new introduction"Andrew Cropper, Sebastijan Dumančić

Journal of Artificial Intelligence Research (JAIR), 2022The paper explains how ILP learns symbolic rules from labeled examples plus background knowledge, and it breaks down ILP system design into learning settings, bias/representation choices, and search strategies. It also surveys major systems and practical limitations, framing modern ILP around solver-backed search, recursion, and predicate invention.https://arxiv.org/abs/2008.07912About the Guest -

Andrew Cropper is an Associate Professor at the University of Helsinki and a principal investigator at ELLIS Institute Finland, where he works on combining logical reasoning with machine learning. His research centers on inductive logic programming and on building high-performance ILP systems (including Popper) that leverage modern SAT/ASP/MaxSAT solving to learn interpretable rules from data.https://andrewcropper.com/Credits -

• Host & Music: Bryan Landers, Technical Staff, Ndea

• Editor: Alejandro Ramirez

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