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摘要翻译：
随着时间的推移，涉及对象和关系的创建的随机过程广泛存在，但研究相对较少。例如，在工厂装配过程中，准确的故障诊断需要推断装配操作错误的概率，但要有效而准确地进行故障诊断是很困难的。这些过程被建模为动态贝叶斯网络，具有非常大的域和极高的维数的离散变量。本文介绍了动态贝叶斯网络(RDBNs)，它是动态贝叶斯网络(DBNs)对一阶逻辑的扩展。RDBNs是动态概率关系模型(DPRMs)的推广，它是我们在以前的工作中提出的用于动态不确定领域建模的模型。我们首先将我们早期工作中描述的RAO-Blackwellished粒子滤波扩展到RDBNs。接下来，我们提出了RDBNs中与Rao-Blackwellidation相关的假设，并提出了两种新的粒子滤波形式。第一种方法在谓词上使用抽象层次结构来平滑粒子滤波估计。第二种方法采用核密度估计，核函数是专为关系域设计的。实验表明，这两种方法在装配计划执行监控方面的性能明显优于标准粒子滤波。
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英文标题：
《Relational Dynamic Bayesian Networks》
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作者：
P. Domingos, S. Sanghai, D. Weld
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最新提交年份：
2011
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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：
Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We first extend the Rao-Blackwellised particle filtering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle filtering. The first one uses abstraction hierarchies over the predicates to smooth the particle filters estimates. The second employs kernel density estimation with a kernel function specifically designed for relational domains. Experiments show these two methods greatly outperform standard particle filtering on the task of assembly plan execution monitoring.
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PDF链接：
https://arxiv.org/pdf/1109.2137
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