1 code implementation • 18 Apr 2019 • Andrew Cropper
In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge.
2 code implementations • 23 Jun 2019 • Andrew Cropper, Richard Evans, Mark Law
This problem is central to inductive general game playing (IGGP).
2 code implementations • 25 Jul 2019 • Andrew Cropper, Rolf Morel, Stephen H. Muggleton
Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs which in turn reduces the size of the hypothesis space and sample complexity.
no code implementations • 25 Jul 2019 • Andrew Cropper, Sophie Tourret
In general, derivation reduced sets of metarules outperforms subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.
2 code implementations • 15 Nov 2019 • Andrew Cropper
To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK.
no code implementations • 25 Feb 2020 • Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
1 code implementation • 21 Apr 2020 • Sebastijan Dumancic, Tias Guns, Andrew Cropper
We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it.
no code implementations • 21 Apr 2020 • Andrew Cropper, Sebastijan Dumančić
We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search.
1 code implementation • 5 May 2020 • Andrew Cropper, Rolf Morel
In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain.
3 code implementations • 18 Aug 2020 • Andrew Cropper, Sebastijan Dumančić
Inductive logic programming (ILP) is a form of machine learning.
no code implementations • 18 Feb 2021 • Rolf Morel, Andrew Cropper
We show that fine-grained failure analysis allows for learning fine-grained constraints on the hypothesis space.
no code implementations • 21 Feb 2021 • Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen H. Muggleton
Inductive logic programming (ILP) is a form of logic-based machine learning.
1 code implementation • 29 Apr 2021 • Andrew Cropper, Rolf Morel
Discovering novel high-level concepts is one of the most important steps needed for human-level AI.
no code implementations • 15 Sep 2021 • Andrew Cropper, Oghenejokpeme Orhobor, Cristian Dinu, Rolf Morel
Multi-core machines are ubiquitous.
1 code implementation • 16 Sep 2021 • Andrew Cropper
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search.
1 code implementation • 20 Feb 2022 • Andrew Cropper, Céline Hocquette
We use the constraints to bootstrap a constraint-driven ILP system.
no code implementations • 1 Jun 2022 • Andrew Cropper, Céline Hocquette
The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples.
2 code implementations • 5 Aug 2022 • Céline Hocquette, Andrew Cropper
A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice.
no code implementations • 24 Aug 2022 • Bogdan Cretu, Andrew Cropper
Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge.
1 code implementation • 3 Oct 2022 • Céline Hocquette, Andrew Cropper
Our approach can identify numerical values in linear arithmetic fragments, such as real difference logic, and from infinite domains, such as real numbers or integers.
1 code implementation • 18 Jan 2023 • David M. Cerna, Andrew Cropper
The ability to generalise from a small number of examples is a fundamental challenge in machine learning.
no code implementations • 16 Aug 2023 • Céline Hocquette, Sebastijan Dumančić, Andrew Cropper
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold.
1 code implementation • 18 Aug 2023 • Céline Hocquette, Andreas Niskanen, Matti Järvisalo, Andrew Cropper
Many inductive logic programming approaches struggle to learn programs from noisy data.
no code implementations • 29 Jan 2024 • Andrew Cropper, Céline Hocquette
The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge.
no code implementations • 29 Jan 2024 • Céline Hocquette, Andreas Niskanen, Rolf Morel, Matti Järvisalo, Andrew Cropper
A major challenge in inductive logic programming is learning big rules.