Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. We evaluate our approach on two program induction domains: real-world string transformations and building Lego structures. Our experiments show that learning from refactored knowledge can improve predictive accuracies fourfold and reduce learning times by half.

翻译：人类不断重组知识,以便更高效地使用知识。 我们的目标是给机器学习系统类似的能力, 以便更高效地学习。 我们引入了“ 知识重组”问题, 目标是重组学习者的知识库, 以缩小其规模, 并最大限度地减少其中的冗余。 我们侧重于感化逻辑程序, 知识基础是一个逻辑程序。 我们引入了 Knorf, 该系统用限制优化解决再影响问题。 我们评估了我们两个程序入门领域的方法: 真实世界的弦转换和构建乐高结构。 我们的实验显示, 从重新构思的知识中学习可以提高预测的精度四倍, 并将学习时间减半 。