Author name cluster

Céline Hocquette

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10 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Relational Decomposition for Program Synthesis

Céline Hocquette
Andrew Cropper

We introduce a relational approach to program synthesis. The key idea is to decompose synthesis tasks into simpler relational synthesis subtasks. Specifically, our representation decomposes a training input-output example into sets of input and output facts respectively. We then learn relations between the input and output facts. We demonstrate our approach using an off-the-shelf inductive logic programming (ILP) system on four challenging synthesis datasets. Our results show that (i) our representation can outperform a standard one, and (ii) an off-the-shelf ILP system with our representation can outperform domain-specific approaches.

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IJCAI Conference 2024 Conference Paper

Learning Big Logical Rules by Joining Small Rules

Céline Hocquette
Andreas Niskanen
Rolf Morel
Matti Järvisalo
Andrew Cropper

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 implement our approach in a constraint-driven system and use constraint solvers to efficiently join rules. Our experiments on many domains, including game playing and drug design, show that our approach can (i) learn rules with more than 100 literals, and (ii) drastically outperform existing approaches in terms of predictive accuracies.

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IJCAI Conference 2024 Conference Paper

Learning Logic Programs by Discovering Higher-Order Abstractions

Céline Hocquette
Sebastijan Dumancic
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 implement our approach in Stevie, which formulates the refactoring problem as a constraint optimisation problem. Our experiments on multiple domains, including program synthesis and visual reasoning, show that refactoring can improve the learning performance of an inductive logic programming system, specifically improving predictive accuracies by 27% and reducing learning times by 47%. We also show that Stevie can discover abstractions that transfer to multiple domains.

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ECAI Conference 2024 Conference Paper

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, with given background knowledge, generalises training examples. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%.

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AAAI Conference 2024 Conference Paper

Learning MDL Logic Programs from Noisy Data

Céline Hocquette
Andreas Niskanen
Matti Järvisalo
Andrew Cropper

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 programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

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ECAI Conference 2023 Conference Paper

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 a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our approach can learn optimal and recursive programs and perform predicate invention. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches in terms of predictive accuracies and learning times, sometimes reducing learning times from over an hour to a few seconds.

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AAAI Conference 2023 Conference Paper

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 an approach that, before searching for a hypothesis, first discovers "where not to search". We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts.

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AAAI Conference 2023 Conference Paper

Relational Program Synthesis with Numerical Reasoning

Céline Hocquette
Andrew Cropper

Learning programs with numerical values is fundamental to many AI applications, including bio-informatics and drug design. However, current program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NumSynth, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values. 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. Our experiments on four diverse domains, including game playing and program synthesis, show that our approach can (i) learn programs with numerical values from linear arithmetical reasoning, and (ii) outperform existing approaches in terms of predictive accuracies and learning times.

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IJCAI Conference 2020 Conference Paper

Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning

Céline Hocquette
Stephen H. Muggleton

Predicate Invention in Meta-Interpretive Learning (MIL) is generally based on a top-down approach, and the search for a consistent hypothesis is carried out starting from the positive examples as goals. We consider augmenting top-down MIL systems with a bottom-up step during which the background knowledge is generalised with an extension of the immediate consequence operator for second-order logic programs. This new method provides a way to perform extensive predicate invention useful for feature discovery. We demonstrate this method is complete with respect to a fragment of dyadic datalog. We theoretically prove this method reduces the number of clauses to be learned for the top-down learner, which in turn can reduce the sample complexity. We formalise an equivalence relation for predicates which is used to eliminate redundant predicates. Our experimental results suggest pairing the state-of-the-art MIL system Metagol with an initial bottom-up step can significantly improve learning performance.

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IJCAI Conference 2019 Conference Paper

Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?

Céline Hocquette

World-class human players have been outperformed in a number of complex two person games such as Go by Deep Reinforcement Learning systems GO. However, several drawbacks can be identified for these systems: 1) The data efficiency is unclear given they appear to require far more training games to achieve such performance than any human player might experience in a lifetime. 2) These systems are not easily interpretable as they provide limited explanation about how decisions are made. 3) These systems do not provide transferability of the learned strategies to other games. We study in this work how an explicit logical representation can overcome these limitations and introduce a new logical system called MIGO designed for learning two player game optimal strategies. It benefits from a strong inductive bias which provides the capability to learn efficiently from a few examples of games played. Additionally, MIGO's learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning.

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