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Inductive logic programming at 30
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DOI：10.1007/s10994-021-06089-1.png)
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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.
Keyword:
Inductive logic programming
Relational learning
Program synthesis
Program induction
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
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