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摘要翻译：
本文研究了一种具有概率逻辑灵活性和关系贝叶斯网络紧凑性的表示语言。目标是处理命题和一阶结构与精确的，不精确的，不确定的和定性的概率评估。本文介绍了如何通过信用网络理论来实现这一目标。提出了基于多线性规划和区间概率迭代/循环传播的精确和近似推理新算法；经验表明，与现有的方法相比，它们的性能优越。
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英文标题：
《Propositional and Relational Bayesian Networks Associated with Imprecise
and Qualitative Probabilistic Assesments》
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作者：
Fabio Gagliardi Cozman, Cassio Polpo de Campos, Jaime Ide, Jose Carlos
Ferreira da Rocha
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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：
This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.
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PDF链接：
https://arxiv.org/pdf/1207.4121
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