This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

中文翻译：

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概率规则学习系统

该调查概述了可以学习不确定域的概率规则结构的规则学习系统。这些系统在这些领域非常有用，因为它们可以用少量的正例和负例进行训练，使用背景知识的声明性表示，并将有效的高级推理与概率论相结合。这些系统的输出是易于人类理解的概率规则，因为结果的条件导致预测变得透明和可解释。该调查侧重于表示方法和系统架构，并提出了未来的研究方向。