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，用内涵谓词干扰假设空间通常会降低性能。相比之下，梯度下降提供了一种在高维空间内高效寻找解决方案的方法。神经符号ILP方法迄今尚未充分利用这一优势。我们提出将{\delta}ILP方法扩展到具有大规模谓词发明的归纳合成中，从而利用高维梯度下降的有效性。我们证明，大规模谓词发明通过梯度下降有利于可微归纳合成，并能够学习超出现有神经符号ILP系统能力范围的任务解决方案。此外，我们在未指定语言偏误中解决方案精确结构的情况下实现了这些结果。