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首页> 外文期刊>Theory and Practice of Logic Programming >CP-logic: A language of causal probabilistic events and its relation to logic programming
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CP-logic: A language of causal probabilistic events and its relation to logic programming

机译：CP-logic：因果概率事件的一种语言及其与逻辑编程的关系

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This paper develops a logical language for representing probabilistic causal laws. Our interest in such a language is two-fold. 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.
机译：本文开发了一种表示概率因果律的逻辑语言。我们对这种语言的兴趣有两个方面。首先，它可以作为对因果知识表示的基础研究的动力。因果关系具有内在的动态方面，Shafer在概率树的框架中已从语义层面对其进行了研究。在这样一个动态的环境中，考虑了域随时间的演变，因果定律作为指导这种演变的某种思想是很自然的。在我们的形式化过程中，可以使用一组概率因果律以简洁，灵活和模块化的方式表示一类概率树。通过这种方式，我们的工作通过为Shafer的语义对象提供方便的逻辑表示而扩展了Shafer。其次，该语言还与概率逻辑编程领域相关。特别是，我们证明了我们语言中一种理论的形式语义可以等效地定义为某些逻辑程序的良好模型上的概率分布，从而使其在形式上与现有语言（如ICL或PRISM）非常相似。因为我们可以以完全自成体系的方式来激发和解释我们的语言，以表示概率因果律，所以这提供了一种新的方式来解释这种概率逻辑程序背后的直觉：我们可以准确地说出这种程序所表达的知识非逻辑学家同样可以理解的术语。此外，通过展示概率逻辑程序如何表达概率因果律，我们还获得了另一套知识表示方法。

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