论文标题
30岁：归纳逻辑编程中的新想法
Turning 30: New Ideas in Inductive Logic Programming
论文作者
论文摘要
对最先进的机器学习的常见批评包括概括不良，缺乏解释性以及需要大量培训数据。我们调查了归纳逻辑编程（ILP）的最新工作，这是一种机器学习的一种形式，从数据中诱导逻辑程序，这表明了解决这些限制方面的希望。我们专注于学习递归程序的新方法，这些方法从少数示例中概括，从使用手工制作的背景知识转变为\ emph {Learning}背景知识以及使用不同技术的使用，尤其是答案设置的编程和神经网络。随着ILP接近30，我们还讨论了未来研究的方向。
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.
