Relational Decomposition for Program Synthesis
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4526-4534.
https://doi.org/10.24963/ijcai.2025/504
Keywords
- Knowledge Representation and Reasoning: KRR: Learning and reasoning
- Knowledge Representation and Reasoning: KRR: Logic programming
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.
@inproceedings{ijcai2025p504,
title = {Relational Decomposition for Program Synthesis},
author = {Hocquette, Céline and Cropper, Andrew},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {4526--4534},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/504},
url = {https://doi.org/10.24963/ijcai.2025/504},
}
TY - CPAPER TI - Relational Decomposition for Program Synthesis AU - Hocquette, Céline AU - Cropper, Andrew T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 4526 EP - 4534 DO - 10.24963/ijcai.2025/504 UR - https://doi.org/10.24963/ijcai.2025/504 ER -Copied
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