---

The Logic and Learning (LOL) group is in the computer science department at the University of Oxford. We work on logic and machine learning, i.e. inductive logic programming (ILP). We develop the ILP system Popper.

We are recruiting multiple postdocs to start in 2022, 2023, and 2024 to work on the The Automatic Computer Scientist project. We are looking for at least one expert in constraint satisfaction problems (SAT/ASP/CP). If interested, please see the join page.

We also are looking for DPhil (PhD) students to join in the Autumn of 2023. If interested, please see the join page.

---

Publications

Journals

1. Learning programs with magic values
   C. Hocquette and A. Cropper
   Machine learning 2022
2. Inductive logic programming at 30: a new introduction
   A. Cropper and S. Dumančić.
   JAIR 2022
3. Inductive logic programming at 30
   A. Cropper, S. Dumančić, R. Evans, and S.H. Muggleton
   Machine learning 2022
4. Learning programs by learning from failures
   A. Cropper and R. Morel
   Machine learning 2021
   
5. Inductive general game playing
   A. Cropper, R. Evans, and M. Law
   Machine learning 2020
   slides code dataset
6. Logical minimisation of metarules
   A. Cropper and S. Tourret
   Machine learning 2020
   slides code
7. Learning higher-order logic programs
   A. Cropper, R. Morel, and S.H. Muggleton
   Machine learning 2020
   slides code
8. Learning efficient logic programs
   A. Cropper and S.H. Muggleton
   Machine learning 2019
   slides code

Conferences

1. Relational program synthesis with numerical reasoning
   C. Hocquette and A. Cropper
   AAAI 2023
2. Learning logic programs by discovering where not to search
   A. Cropper and C. Hocquette
   AAAI 2023
3. Learning logic programs through divide, constrain, and conquer
   A. Cropper
   AAAI 2022
4. Knowledge refactoring for inductive program synthesis
   S. Dumančić, T. Guns, and A. Cropper
   AAAI 2021
5. Learning large logic programs by going beyond entailment
   A. Cropper and S. Dumančić
   IJCAI 2020
6. Turning 30: new ideas in inductive logic programming
   A. Cropper, S. Dumančić, and S.H. Muggleton
   IJCAI 2020
7. Forgetting to learn logic programs
   A. Cropper
   AAAI 2020
   slides code
8. Learning higher-order programs through predicate invention
   A. Cropper, R. Morel, and S.H. Muggleton
   AAAI 2020
   slides
9. Playgol: learning programs through play
   A. Cropper
   IJCAI 2019
   slides code
10. SLD-resolution reduction of second-order horn fragments
    S. Tourret and A. Cropper
    JELIA 2019
11. Typed meta-interpretive learning of logic programs
    R. Morel, A. Cropper, and L. Ong
    JELIA 2019
    slides code
12. Derivation reduction of metarules in meta-interpretive learning
    A. Cropper and S. Tourret
    ILP 2018
    slides code
13. Learning higher-order logic programs through abstraction and invention
    A. Cropper and S.H. Muggleton
    IJCAI 2016
    slides code
14. Logic-based inductive synthesis of efficient programs
    A. Cropper
    IJCAI 2016
    slides
15. Learning efficient logical robot strategies involving composable objects
    A. Cropper and S.H. Muggleton
    IJCAI 2015
    slides code
16. Learning efficient logic programs
    A. Cropper
    IJCAI 2015
    slides
17. Meta-interpretive learning of data transformation programs
    A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton
    ILP 2015
    slides code
18. Typed meta-interpretive learning for proof strategies
    C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
    ILP 2015
19. Can predicate invention compensate for incomplete background knowledge?
    A. Cropper and S.H. Muggleton
    SCAI 2015
    slides
20. Logical minimisation of meta-rules within meta-interpretive learning
    A. Cropper and S.H. Muggleton
    ILP 2014
    slides