Relational decomposition for program synthesis

• URL: http://arxiv.org/abs/2408.12212v2
• Date: Thu, 06 Feb 2025 14:58:03 GMT
• Title: Relational decomposition for program synthesis
• Authors: Céline Hocquette, Andrew Cropper,
• Abstract summary: We introduce a relational approach to program synthesis. Specifically, our representation decomposes a training input-output example into sets of input and output facts respectively. We demonstrate our approach using an off-the-shelf inductive logic programming (ILP) system on four challenging synthesis datasets.
• Score: 27.6240219662896
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• 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.

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