Relational decomposition for program synthesis

TL;DR

This paper presents a relational decomposition approach to program synthesis, breaking down tasks into simpler subtasks and leveraging ILP systems to improve performance on challenging datasets.

Contribution

It introduces a novel relational representation for program synthesis that enhances existing ILP systems and outperforms domain-specific methods.

Findings

Relational representation improves synthesis performance

Off-the-shelf ILP with our approach outperforms domain-specific methods

Our method excels on challenging synthesis datasets

Abstract

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.