Probabilistic Inductive Logic Programming - Theory and Applications
Probabilistic Inductive Logic Programming - Theory and Applications
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DOI：
10.1007/978-3-540-78652-8
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发表时间：
2008
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概率归纳逻辑编程。统计关系学习解决了人工智能的中心问题之一：概率推理与机器学习以及一阶和关系逻辑表示的集成。丰富多样的不同的形式主义和学习技术已经开发出来。在本章中，我们将从归纳逻辑编程开始，并概述归纳逻辑编程的形式主义、设置和技术如何扩展到统计案例。更确切地说，我们概述了归纳逻辑编程的三个经典设置，即从蕴涵学习，从解释学习，从证明或痕迹学习，并展示了它们如何适应涵盖最先进的统计关系学习方法。
Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far.In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namelylearning from entailment,learning from interpretations, andlearning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.