Inductive logic programming at 30
Inductive logic programming at 30
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DOI：
10.1007/s10994-021-06089-1
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发表时间：
2021-11-09
期刊：
影响因子：
7.5
通讯作者：
Muggleton, Stephen H.
中科院分区：
文献类型：
--
作者：
Cropper, Andrew;Dumancic, Sebastijan;Muggleton, Stephen H.
归纳逻辑编程(ILP)是一种基于逻辑的机器学习。目标是归纳一个假设(逻辑程序)，概括给定的训练示例和背景知识。在ILP 30岁生日之际，我们回顾了过去十年的研究。我们的重点是(I)新的元级搜索方法，(Ii)学习递归程序的技术，(Iii)谓词发明的新方法，以及(Iv)不同技术的使用。最后，我们讨论了ILP目前的局限性和未来的研究方向。
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.