We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.

翻译：我们引入了一种将传统的分化和征服搜索与现代约束驱动搜索相结合的感官逻辑编程方法。 我们的随时方法可以学习最佳、循环和大型程序,支持上游发明。 我们在三个领域的实验(分类、感化一般游戏以及程序合成)表明,我们的方法可以增加预测的灵敏度并缩短学习时间。