通过剔除无用规则，大幅提升逻辑编程的推理效率。
Efficient rule induction by ignoring pointless rules
- 识别并忽略冗余或无法区分负例的无用规则
- 在多个领域将学习时间减少99%，准确率不变
- 适合需要高效规则生成的推理系统开发者
归纳逻辑编程（ILP）的目标是找到一组能概括训练样本和背景知识的逻辑规则。本文提出一种新方法，可识别无用规则：若规则包含冗余谓词或无法区分负例，则视为无用。证明忽略此类规则可在不丢失正确性的前提下有效剪枝假设空间。在视觉推理和游戏博弈等多个领域实验表明，该方法可将学习时间降低99%且保持预测准确率。
原文摘要 · Abstract (English)
The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that ignoring pointless rules allows an ILP system to soundly prune the hypothesis space. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce learning times by 99% whilst maintaining predictive accuracies.
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