7 citations · 19 across the 17 of their papers we have counts for
25 papers
Learning logic programs by finding minimal unsatisfiable subprograms
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. We introduce an ILP approach that identi…
Learning big logical rules by joining small rules
Céline Hocquette, Andreas Niskanen, Rolf Morel +2
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We impleme…
Learning MDL logic programs from noisy data
Céline Hocquette, Andreas Niskanen, Matti Järvisalo +1
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length…
Learning logic programs by discovering higher-order abstractions
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. We impleme…
Generalisation Through Negation and Predicate Invention
David M. Cerna, Andrew Cropper
The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP…
Relational program synthesis with numerical reasoning
Céline Hocquette, Andrew Cropper
Program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values over multiple examples, such as interva…
Learning programs with magic values
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. Learning programs with magic values…
Constraint-driven multi-task learning
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. In this project, w…
Learning logic programs by combining programs
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. Inducing programs with many rules and literals is…
Learning logic programs by discovering where not to search
Andrew Cropper, Céline Hocquette
The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce a…
Parallel Constraint-Driven Inductive Logic Programming
Andrew Cropper, Oghenejokpeme Orhobor, Cristian Dinu +1
Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limi…
Learning logic programs through divide, constrain, and conquer
Andrew Cropper
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal,…
Predicate Invention by Learning From Failures
Andrew Cropper, Rolf Morel
Discovering novel high-level concepts is one of the most important steps needed for human-level AI. In inductive logic programming (ILP), discovering novel high-level concepts is k…
Learning logic programs by explaining their failures
Rolf Morel, Andrew Cropper
Scientists form hypotheses and experimentally test them. If a hypothesis fails (is refuted), scientists try to explain the failure to eliminate other hypotheses. The more precise t…
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…
Inductive logic programming at 30: a new introduction
Andrew Cropper, Sebastijan Dumančić
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns…
Learning programs by learning from failures
Andrew Cropper, Rolf Morel
We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three se…
Knowledge Refactoring for Inductive Program Synthesis
Sebastijan Dumancic, Tias Guns, Andrew Cropper
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introd…
Turning 30: New Ideas in Inductive Logic Programming
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. We survey recent work…
Forgetting to learn logic programs
Andrew Cropper
Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK…
Learning higher-order logic programs
Andrew Cropper, Rolf Morel, Stephen H. Muggleton
A key feature of inductive logic programming (ILP) is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper,…
Logical reduction of metarules
Andrew Cropper, Sophie Tourret
Many forms of inductive logic programming (ILP) use \emph{metarules}, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Decidi…
Inductive general game playing
Andrew Cropper, Richard Evans, Mark Law
General game playing (GGP) is a framework for evaluating an agent's general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game…
Playgol: learning programs through play
Andrew Cropper
Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of tasks an…