通过忽略无用规则实现高效的规则归纳
Efficient rule induction by ignoring pointless rules
AI总结：
本文提出了一种通过忽略无用规则来提升归纳逻辑编程效率的方法，实验表明该方法在多个领域显著减少了学习时间并保持了预测准确性。
摘要：
归纳逻辑编程（ILP）的目标是找到一组逻辑规则，这些规则能够概括训练示例和背景知识。我们介绍了一种ILP方法，该方法能够识别无用规则。一个规则如果包含冗余的谓词或无法区分负例，则被认为是无用的。我们证明，忽略无用规则使ILP系统能够对假设空间进行有效的剪枝。我们在多个领域，包括视觉推理和游戏玩法中进行了实验，结果显示，我们的方法可以将学习时间减少99%，同时保持预测准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
