Inductive logic programming at 30

被引：25

作者：

Cropper, Andrew ^{[1
]}

Dumancic, Sebastijan ^{[2
]}

Evans, Richard ^{[3
]}

Muggleton, Stephen H. ^{[3
]}

机构：

[1] Univ Oxford, Oxford, England

[2] Katholieke Univ Leuven, Leuven, Belgium

[3] Imperial Coll London, London, England

来源：

MACHINE LEARNING | 2022年 / 111卷 / 01期

基金：

英国工程与自然科学研究理事会;

关键词：

Inductive logic programming; Relational learning; Program synthesis; Program induction; PREDICATE INVENTION; DEFINITIONS; GENERATION; DISCOVERY;

D O I：

10.1007/s10994-021-06089-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：147 / 172

页数：26

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