Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades performance. In contrast, gradient descent provides an efficient way to find solutions within such high- dimensional spaces. Neuro-symbolic ILP approaches have not fully exploited this so far. We propose extending the {\delta}ILP approach to inductive synthesis with large-scale predicate invention, thus allowing us to exploit the efficacy of high-dimensional gradient descent. We show that large-scale predicate invention benefits differentiable inductive synthesis through gradient descent and allows one to learn solutions for tasks beyond the capabilities of existing neuro-symbolic ILP systems. Furthermore, we achieve these results without specifying the precise structure of the solution within the language bias.

翻译：通过符号归纳逻辑编程（ILP）合成大型逻辑程序通常需要中间定义。然而，用内涵谓词填充假设空间通常会降低性能。相比之下，梯度下降为在这种高维空间中找到解决方案提供了高效途径。神经符号ILP方法迄今为止尚未充分利用这一点。我们提出将δILP方法扩展到大规模谓词发明的归纳合成中，从而利用高维梯度下降的高效性。我们证明，大规模谓词发明通过梯度下降有益于可微归纳合成，并允许学习超出现有神经符号ILP系统能力的任务解决方案。此外，我们在不指定语言偏误中解决方案的具体结构的情况下实现了这些结果。