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.

翻译：归纳逻辑规划（ILP）的目标是找到一组逻辑规则，以泛化训练示例和背景知识。本文提出一种识别无意义规则的ILP方法。若某规则包含冗余文字或无法区分负例，则视为无意义。我们证明忽略无意义规则可使ILP系统可靠地剪枝假设空间。在视觉推理与游戏博弈等多个领域的实验表明，该方法能在保持预测准确率的同时将学习时间降低99%。