Relational decomposition for program synthesis
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910996931543040 |
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| author | Hocquette, Céline Cropper, Andrew |
| author_facet | Hocquette, Céline Cropper, Andrew |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_12212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Relational decomposition for program synthesis Hocquette, Céline Cropper, Andrew Artificial Intelligence Machine Learning 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. |
| title | Relational decomposition for program synthesis |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.12212 |