1 1 Relational decomposition for program synthesis

id:: 2408.12212
Authors:: Céline Hocquette, Andrew Cropper
Published:: 2024-08-22
arXiv:: https://arxiv.org/abs/2408.12212
PDF:: https://arxiv.org/pdf/2408.12212
DOI:: N/A
Journal Reference:: N/A
Primary Category:: cs.AI
Categories:: cs.AI, cs.LG
Comment:: N/A
github_url:: _

1.1 1.1 abstract

We introduce a novel approach to program synthesis that decomposes complex functional tasks into simpler relational synthesis sub-tasks. We demonstrate the effectiveness of our approach using an off-the-shelf inductive logic programming (ILP) system on three challenging datasets. Our results show that (i) a relational representation can outperform a functional one, and (ii) an off-the-shelf ILP system with a relational encoding can outperform domain-specific approaches.

1.1.1 1.1.1 premise

1.1.2 1.1.2 outline

1.1.3 1.1.3 quotes

1.1.4 1.1.4 notes

1.2 1.2 summary

1.2.1 1.2.1 1. Brief Overview

This paper introduces a novel approach to program synthesis that decomposes complex functional tasks into simpler relational synthesis sub-tasks. The authors demonstrate the effectiveness of their approach using an off-the-shelf inductive logic programming (ILP) system on three challenging datasets (ID-ARC, ARC, and List functions). Their results show that a relational representation can outperform a functional one, and that an off-the-shelf ILP system with a relational encoding can outperform domain-specific approaches. The core contribution is showing that decomposing complex functional synthesis tasks into relational sub-tasks simplifies the overall learning process.

1.2.2 1.2.2 2. Key Points

A novel program synthesis approach is introduced that decomposes functional tasks into multiple relational tasks.
An off-the-shelf ILP system with a relational representation achieves high performance compared to domain-specific approaches across multiple datasets (ID-ARC, ARC, List functions).
Relational representation significantly outperforms functional representation in program synthesis across all tested datasets.
The relational approach’s efficiency stems from decomposing each training example into multiple smaller examples, allowing independent rule learning and subsequent combination.
The method effectively handles long sequences of functions, addressing a significant challenge in existing functional approaches.

1.2.3 1.2.3 3. Notable Quotes

No notable quotes were identified in the provided text.

1.2.4 1.2.4 4. Primary Themes

Relational vs. Functional Program Synthesis: The paper’s central theme is a comparison of relational and functional approaches to program synthesis. It argues that a relational approach, by decomposing complex problems into smaller, interrelated subproblems, provides significant advantages in terms of learning efficiency and scalability.
Inductive Logic Programming (ILP): ILP plays a crucial role as the underlying framework for the relational approach. The authors highlight the effectiveness of using an off-the-shelf ILP system with minimal domain-specific modifications.
General-Purpose vs. Domain-Specific Approaches: The study demonstrates that a general-purpose approach, leveraging the relational decomposition technique and an off-the-shelf ILP system, can achieve competitive or superior performance compared to domain-specific methods tailored to specific datasets (like ARC or List functions).
Benchmark Datasets: The paper uses three diverse and challenging benchmark datasets to evaluate the performance of their approach, making the results robust and widely applicable to program synthesis tasks.