Discovering novel abstractions is important for human-level AI. We introduce
an approach to discover higher-order abstractions, such as map, filter, and
fold. We focus on inductive logic programming, which induces logic programs
from examples and background knowledge. We introduce the higher-order
refactoring problem, where the goal is to compress a logic program by
introducing higher-order abstractions. We implement our approach in STEVIE,
which formulates the higher-order refactoring problem as a constraint
optimisation problem. Our experimental results on multiple domains, including
program synthesis and visual reasoning, show that, compared to no refactoring,
STEVIE can improve predictive accuracies by 27% and reduce learning times by
47%. We also show that STEVIE can discover abstractions that transfer to
different domains

通过引入高阶抽象概念来发现新颖的抽象是实现人工智能的重要任务。我们介绍了一种发现高阶抽象（例如映射、过滤和折叠）的方法，并以归纳逻辑编程为重点，通过示例和背景知识归纳出逻辑程序。我们在 STEVIE 中实现了我们的方法，将高阶重构问题作为约束优化问题来处理。在多个领域（包括程序合成和视觉推理）上的实验结果表明，与不进行重构相比，STEVIE 可以提高预测准确性 27%，并减少学习时间 47%。我们还展示了 STEVIE 能够发现可应用于不同领域的抽象概念。