论文标题
归纳逻辑编程在30：新简介
Inductive logic programming at 30: a new introduction
论文作者
论文摘要
归纳逻辑编程（ILP）是机器学习的一种形式。 ILP的目的是诱导概括培训示例的假设（一组逻辑规则）。随着ILP 30岁，我们为该领域提供了新的介绍。我们介绍了必要的逻辑符号和主要的学习设置；描述ILP系统的基础；比较几个维度上的几个系统；描述四个系统（Aleph，Tilde，Aspal和Metagol）；突出显示关键应用领域；最后，总结了当前的局限性和未来研究的方向。
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.
