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.

翻译：我们提出了一种新颖的程序合成方法，该方法将复杂的功能性任务分解为更简单的关联合成子任务。我们通过使用现成的归纳逻辑编程（ILP）系统在三个具有挑战性的数据集上验证了该方法的有效性。我们的结果表明：（i）关联表示的性能可以优于功能表示；（ii）采用关联编码的现成ILP系统可以超越特定领域的方法。