Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

翻译：许多归纳逻辑编程方法难以从含噪声数据中学习程序。为克服这一局限，我们提出了一种从含噪声数据中学习最小描述长度程序的方法，包括递归程序。在药物设计、游戏博弈和程序合成等多个领域的实验表明，我们的方法在预测准确性上优于现有方法，并能适应中等程度的噪声规模。