Turning 30: New Ideas in Inductive Logic Programming
Turning 30: New Ideas in Inductive Logic Programming
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DOI：
10.24963/ijcai.2020/673
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Andrew Cropper;Sebastijan Dumancic;Stephen Muggleton
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文献类型：
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作者：
Andrew Cropper;Sebastijan Dumancic;Stephen Muggleton
对最先进的机器学习的常见批评包括泛化能力差，缺乏可解释性，以及需要大量的训练数据。我们调查了归纳逻辑编程（ILP）方面的最新工作，这是一种从数据中归纳逻辑程序的机器学习形式，它在解决这些限制方面显示出了希望。我们专注于学习递归程序的新方法，从几个例子中概括，从使用手工制作的背景知识到学习背景知识的转变，以及使用不同的技术，特别是答案集编程和神经网络。随着ILP接近30，我们还讨论了未来的研究方向。
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 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.