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
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Learning programs with magic values
C. Hocquette and A. Cropper
Machine learning 2022
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Inductive logic programming at 30: a new introduction
A. Cropper and S. Dumančić.
JAIR 2022
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Inductive logic programming at 30
A. Cropper, S. Dumančić, R. Evans, and S.H. Muggleton
Machine learning 2022
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Learning programs by learning from failures
A. Cropper and R. Morel
Machine learning 2021
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Inductive general game playing
A. Cropper, R. Evans, and M. Law
Machine learning 2020
slides
code
dataset
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Logical minimisation of metarules
A. Cropper and S. Tourret
Machine learning 2020
slides
code
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Learning higher-order logic programs
A. Cropper, R. Morel, and S.H. Muggleton
Machine learning 2020
slides
code
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Learning efficient logic programs
A. Cropper and S.H. Muggleton
Machine learning 2019
slides
code
Conferences
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Relational program synthesis with numerical reasoning
C. Hocquette and A. Cropper
AAAI 2023
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Learning logic programs by discovering where not to search
A. Cropper and C. Hocquette
AAAI 2023
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Learning logic programs through divide, constrain, and conquer
A. Cropper
AAAI 2022
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Knowledge refactoring for inductive program synthesis
S. Dumančić, T. Guns, and A. Cropper
AAAI 2021
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Learning large logic programs by going beyond entailment
A. Cropper and S. Dumančić
IJCAI 2020
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Turning 30: new ideas in inductive logic programming
A. Cropper, S. Dumančić, and S.H. Muggleton
IJCAI 2020
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Forgetting to learn logic programs
A. Cropper
AAAI 2020
slides
code
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Learning higher-order programs through predicate invention
A. Cropper, R. Morel, and S.H. Muggleton
AAAI 2020
slides
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Playgol: learning programs through play
A. Cropper
IJCAI 2019
slides
code
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SLD-resolution reduction of second-order horn fragments
S. Tourret and A. Cropper
JELIA 2019
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Typed meta-interpretive learning of logic programs
R. Morel, A. Cropper, and L. Ong
JELIA 2019
slides
code
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Derivation reduction of metarules in meta-interpretive learning
A. Cropper and S. Tourret
ILP 2018
slides
code
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Learning higher-order logic programs through abstraction and invention
A. Cropper and S.H. Muggleton
IJCAI 2016
slides
code
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Logic-based inductive synthesis of efficient programs
A. Cropper
IJCAI 2016
slides
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Learning efficient logical robot strategies involving composable objects
A. Cropper and S.H. Muggleton
IJCAI 2015
slides
code
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Learning efficient logic programs
A. Cropper
IJCAI 2015
slides
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Meta-interpretive learning of data transformation programs
A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton
ILP 2015
slides
code
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Typed meta-interpretive learning for proof strategies
C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
ILP 2015
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Can predicate invention compensate for incomplete background knowledge?
A. Cropper and S.H. Muggleton
SCAI 2015
slides
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Logical minimisation of meta-rules within meta-interpretive learning
A. Cropper and S.H. Muggleton
ILP 2014
slides