摘要翻译：
在一个开放的世界中，智能系统必须对许多相互作用的实体进行推理，这些实体以不同的方式相互关联，并且具有不确定的特征和关系。传统的概率语言缺乏处理关系域的表达能力。经典的一阶逻辑具有足够的表达能力，但缺乏连贯的似然推理能力。近年来，出现了多种方法来集成一阶逻辑、概率和机器学习。本文提出了多实体贝叶斯网络(MEBN)，这是一个将一阶逻辑(FOL)与贝叶斯概率论相结合的形式化系统。MEBN扩展了普通贝叶斯网络，允许表示具有重复子结构的图形模型，并且可以表示任何一致的、有限公理化的一阶理论模型上的概率分布。我们用一个受派拉蒙系列星际迷航启发的例子来展示这个逻辑。
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英文标题：
《Of Starships and Klingons: Bayesian Logic for the 23rd Century》
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作者：
Kathryn Blackmond Laskey, Paulo da Costa
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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：
  Intelligent systems in an open world must reason about many interacting entities related to each other in diverse ways and having uncertain features and relationships. Traditional probabilistic languages lack the expressive power to handle relational domains. Classical first-order logic is sufficiently expressive, but lacks a coherent plausible reasoning capability. Recent years have seen the emergence of a variety of approaches to integrating first-order logic, probability, and machine learning. This paper presents Multi-entity Bayesian networks (MEBN), a formal system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures, and can express a probability distribution over models of any consistent, finitely axiomatizable first-order theory. We present the logic using an example inspired by the Paramount Series StarTrek.
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PDF链接：
https://arxiv.org/pdf/1207.1354