Stephen Muggleton, Emeritus Professor at Imperial College London, discusses his paper “Inductive Logic Programming”, which introduced and named the field. The paper presents a framework that combines logic programming with machine learning, enabling systems to learn interpretable logical rules from examples and background knowledge.

Muggleton reflects on the intellectual origins of ILP, tracing its development through his PhD work under Donald Michie and his interactions with pioneering figures including John McCarthy, Ross Quinlan, and others from the early AI community. He describes how dissatisfaction with purely propositional learning systems motivated a search for richer representations capable of expressing structured knowledge and supporting scientific discovery.

In This Episode -

• Origins of ILP

• Michie, Turing, and AI research bans

• Logic programming meets machine learning

• Learning from positive examples

• Learning from a single example

• Predicate invention & abstraction

• Robot Scientist research program

• Efficient greedy search algorithms

• ILP & modern large language models

References -

• https://www.doc.ic.ac.uk/~shm/Papers/Reduce.pdf

• https://en.wikipedia.org/wiki/Donald_Michie

• https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)

• https://en.wikipedia.org/wiki/Ross_Quinlan

• https://en.wikipedia.org/wiki/Karl_Popper

About the Paper -

“Inductive Logic Programming”

Author: Stephen Muggleton

Venue: New Generation Computing (1991)

The paper formally introduced inductive logic programming as a research field at the intersection of machine learning and logic programming. It argues that learning systems should be able to construct logical theories using both observed examples and existing background knowledge, enabling more expressive and interpretable forms of machine learning.

https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf

About the Guest -

Stephen Muggleton is Emeritus Professor of Machine Learning at Imperial College London. He is the founder of inductive logic programming and has made foundational contributions to machine learning, scientific discovery systems, program synthesis, and neurosymbolic AI. His research focuses on machine learning, logic-based reasoning, scientific discovery, probabilistic inference, and automated knowledge acquisition.

https://www.doc.ic.ac.uk/~shm/

Credits -

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

• Editor: Alejandro Ramirez

• https://x.com/ndea

• https://x.com/bryanlanders

• https://ndea.com