摘要翻译：
以Sato和Kameya的PRISM、Poole的ICL、Raedt等人的ProbLog和Vennekens等人的LPAD为例的概率逻辑规划(PLP)旨在将统计和逻辑知识表示和推理结合起来。PLP框架的一个重要特点是它们是对已广泛用于知识表示的非概率逻辑程序的保守扩展。PLP框架将传统的逻辑编程语义扩展到分布语义，其中概率逻辑程序的语义是根据程序的可能模型的分布给出的。然而，这些工作中使用的推理技术依赖于对查询答案的一组解释的枚举。因此，这些语言允许非常有限地使用具有连续分布的随机变量。在本文中，我们提出了一个符号推理过程，它使用约束和表示不带枚举的解释集。这使得我们能够在PLPs上用高斯或伽玛分布的随机变量（除了离散值随机变量之外）和在REALS上的线性等式约束进行推理。在PRISM环境下开发了推理程序；然而，该过程的核心思想也可以很容易地应用于其他PLP语言。我们的推理过程的一个有趣的方面是，在程序中没有任何连续随机变量的情况下，Prism的查询求值过程成为一个特例。符号推理过程使我们能够对复杂的概率模型进行推理，如卡尔曼滤波器和混合贝叶斯网络的一个大的子类，这些在PLP框架中是不可能的。（刊登在逻辑程序设计的理论与实践上）。
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英文标题：
《Inference in Probabilistic Logic Programs with Continuous Random
  Variables》
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作者：
Muhammad Asiful Islam, C. R. Ramakrishnan, I. V. Ramakrishnan
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最新提交年份：
2012
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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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英文摘要：
  Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed at combining statistical and logical knowledge representation and inference. A key characteristic of PLP frameworks is that they are conservative extensions to non-probabilistic logic programs which have been widely used for knowledge representation. PLP frameworks extend traditional logic programming semantics to a distribution semantics, where the semantics of a probabilistic logic program is given in terms of a distribution over possible models of the program. However, the inference techniques used in these works rely on enumerating sets of explanations for a query answer. Consequently, these languages permit very limited use of random variables with continuous distributions. In this paper, we present a symbolic inference procedure that uses constraints and represents sets of explanations without enumeration. This permits us to reason over PLPs with Gaussian or Gamma-distributed random variables (in addition to discrete-valued random variables) and linear equality constraints over reals. We develop the inference procedure in the context of PRISM; however the procedure's core ideas can be easily applied to other PLP languages as well. An interesting aspect of our inference procedure is that PRISM's query evaluation process becomes a special case in the absence of any continuous random variables in the program. The symbolic inference procedure enables us to reason over complex probabilistic models such as Kalman filters and a large subclass of Hybrid Bayesian networks that were hitherto not possible in PLP frameworks. (To appear in Theory and Practice of Logic Programming).
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PDF链接：
https://arxiv.org/pdf/1112.2681