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摘要翻译：
本文发展了一种表示概率因果律的逻辑语言。我们对这样一种语言的兴趣是双重的。首先，它可以作为因果知识表示的基础研究。因果关系具有内在的动态性，Shafer在他的概率树框架中对其进行了语义层面的研究。在这样一个动态的语境中，在这个语境中，一个领域随着时间的演变被考虑，因果律作为指导这种演变的东西的想法是非常自然的。在我们的形式化中，一组概率因果律可以用简洁、灵活和模块化的方式来表示一类概率树。通过这种方式，我们的工作扩展了Shafer的工作，为他的语义对象提供了一个方便的逻辑表示。其次，该语言也适用于概率逻辑程序设计领域。特别地，我们证明了我们语言中的理论的形式语义可以等价地定义为某些逻辑程序的有充分基础的模型上的概率分布，从而使它在形式上与现有的语言如ICL或PRISM相当相似。因为我们能够以一种作为概率因果律的表示的完全自足的方式来激励和解释我们的语言，这就提供了一种解释这种概率逻辑程序背后的直觉的新方法：我们能够精确地说出这种程序用非逻辑学家同样可以理解的术语表达了哪些知识。此外，我们还通过展示概率逻辑程序如何表达概率因果律，获得了一种额外的知识表示方法。
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英文标题：
《CP-logic: A Language of Causal Probabilistic Events and Its Relation to
Logic Programming》
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作者：
Joost Vennekens, Marc Denecker, Maurice Bruynooghe
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Logic in Computer Science 计算机科学中的逻辑
分类描述：Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
涵盖计算机科学中逻辑的所有方面，包括有限模型理论，程序逻辑，模态逻辑和程序验证。程序设计语言语义学应该把程序设计语言作为主要的学科领域。大致包括ACM学科类D.2.4、F.3.1、F.4.0、F.4.1和F.4.2中的材料；F.4.3（形式语言）中的一些材料在这里也可能是合适的，尽管计算复杂性通常是更合适的主题领域。
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英文摘要：
This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.
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PDF链接：
https://arxiv.org/pdf/0904.1672
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