通过逻辑预处理缩小假设空间，加速归纳逻辑编程学习。
Honey, I shrunk the hypothesis space (through logical preprocessing)
- 利用背景知识提前排除不可能的规则，压缩搜索空间。
- 预处理仅需10秒，学习时间从10小时缩短至2秒。
- 适合需要快速推理的逻辑学习场景，如视觉推理与游戏策略。
归纳逻辑编程（ILP）是一种逻辑机器学习方法，目标是从假设空间中搜索能泛化训练样本和背景知识的假设。本文提出一种在ILP系统搜索前缩小假设空间的方法：利用背景知识识别出无论训练数据如何都不可能存在于最优假设中的规则，例如“偶数不能是奇数”或“大于2的质数必为奇数”，并将这些违反逻辑的规则从假设空间中移除。我们使用答案集编程实现该方法，并应用于基于约束的ILP系统。在多个领域（包括视觉推理与游戏博弈）的实验表明，该方法显著减少学习时间，同时保持预测准确性。例如，仅用10秒预处理时间，即可将学习时间从超过10小时降至2秒。
原文摘要 · Abstract (English)
Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We introduce an approach that 'shrinks' the hypothesis space before an ILP system searches it. Our approach uses background knowledge to find rules that cannot be in an optimal hypothesis regardless of the training examples. For instance, our approach discovers relationships such as "even numbers cannot be odd" and "prime numbers greater than 2 are odd". It then removes violating rules from the hypothesis space. We implement our approach using answer set programming and use it to shrink the hypothesis space of a constraint-based ILP system. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can substantially reduce learning times whilst maintaining predictive accuracies. For instance, given just 10 seconds of preprocessing time, our approach can reduce learning times from over 10 hours to only 2 seconds.
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