Synthesizing large logic programs through Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates often 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 an approach to ILP-based synthesis benefiting from large-scale predicate invention exploiting the efficacy of high-dimensional gradient descent. We find symbolic solutions containing upwards of ten auxiliary definitions. This is beyond the achievements of existing neuro-symbolic ILP systems, thus constituting a milestone in the field.
翻译:通过感性逻辑编程(ILP)合成大型逻辑程序通常需要中间定义。 但是,如果用强化的上游来打破假设空间,则往往会降低性能。相反,梯度下降为在这种高维空间中找到解决办法提供了有效的方法。到目前为止,神经-同步的ILP方法尚未充分利用这一点。我们提出了一个基于ILP的合成方法,从利用高维梯度下行的功效的大规模上游发明中受益。我们找到了包含10个上层辅助定义的象征性解决方案。这超出了现有神经-同步的ILP系统的成就,因此构成了该领域的一个里程碑。