Oxford University Computing Laboratory

Machine Learning Group

The Group's research is mainly, though not entirely, devoted to Inductive Logic Programming: a form of machine learning in which the learning program constructs general rules from specific examples, together with extra "background knowledge". Examples, rules and background knowledge are expressed in a logic programming language such as Prolog.

We have developed various implementations, of which the most recent is Progol, a Prolog interpreter augmented with an inverse entailment algorithm which constructs new clauses by generalising from nominated examples in the Prolog database. Results from the theory of ILP guarantee that Progol will conduct an admissible search through the space of generalisations, finding the maximally compressive set of clauses from which all the examples can be inferred.

The Group's theoretical work extends beyond such results as the above to various topics in the general theory of machine learning, including U-learnability. We are also researching into applications of ILP, including drug design, protein shape prediction and satellite fault diagnosis.

On the non-ILP front, we are using decision-tree techniques to learn rules for selection of the best embryos for transfer in In Vitro fertilisation. Another project involves the comparison of decision-tree methods with ILP in "reverse-engineering" human skills: designing controllers which emulate human F-16 pilots.

We are involved in a joint Anglo-Japanese research project under the auspices of the Machine Intelligence series. Starting in 1995, the series will continue as the proceedings of Anglo-Japanese workshops on Machine intelligence tools for discovery in science and technology, with emphasis on inductive logic programming, neural networks and related statistical learning techniques, and database mining. These will be applied particularly to problems in molecular biology and in dynamic control.

Finally, we maintain a collection of datasets containing example data arising from our work on applications of ILP. They are provided as a service to machine learning researchers wishing to compare and evaluate different machine learning techniques.

Group Members

Current Projects


We are running a series of Machine Learning Seminars.


We teach a course in logic programming and learning as part of the MSc in Computation.

Students and theses

Ruhi Saith (DPhil student at Nuffield Department of Obstetrics and Gynaecology)
Assessment of human embryo quality
Steve Moyle
Knowledge Extracted from Temporal Data using ILP Techniques
Samual Roberts
Application of Inductive Logic Programming to Road Traffic Flow
Khalid Khan
The Comlpexity of Machine Learning


The publications of the group can be found on a separate page.

ILP workshops' bibliographic details

These can be found on a separate page.

On-line Journals

Other Machine Learning Groups

Machine Learning Forthcoming Events

A list of forthcoming events can be found on a separate page.

ILP Conferece Proceedings

Details of ILP conference proceedings dating back to ILP91 can be found on a separate page.

Other AI research in Oxford
These pages were designed by Jocelyn Paine, and are maintained by Steve Moyle.