The goal of inductive logic programming is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a method to break symmetries in the hypothesis space. We implement our idea in answer set programming. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce solving times from over an hour to just 17 seconds.

翻译：归纳逻辑编程的目标是寻找能够泛化训练数据和背景知识的假设。其挑战在于搜索庞大的假设空间，而大量逻辑等价假设的存在进一步加剧了这一难度。为应对此挑战，我们提出了一种在假设空间中打破对称性的方法。我们在答案集编程中实现了这一思想。在视觉推理和游戏博弈等多个领域的实验表明，我们的方法能将求解时间从超过一小时缩短至仅17秒。