摘要翻译:
使用局部计算来求解影响图有几种体系结构,如Shenoy-Shafer、HUGIN或懒惰传播体系结构。由于使用了所谓的“势”,它们都扩展了通常的变量消除算法。在本文中,我们提出了一种新的结构,称为多算子簇DAG结构,它可以产生具有改进的约束诱导宽度的分解,从而产生潜在的指数增益。它的原则是利用影响图的复合性质,而不是使用统一的势,以便更好地分析问题结构。
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英文标题:
《From influence diagrams to multi-operator cluster DAGs》
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作者:
Cedric Pralet, Thomas Schiex, Gerard Verfaillie
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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:
There exist several architectures to solve influence diagrams using local computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation architectures. They all extend usual variable elimination algorithms thanks to the use of so-called 'potentials'. In this paper, we introduce a new architecture, called the Multi-operator Cluster DAG architecture, which can produce decompositions with an improved constrained induced-width, and therefore induce potentially exponential gains. Its principle is to benefit from the composite nature of influence diagrams, instead of using uniform potentials, in order to better analyze the problem structure.
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PDF链接:
https://arxiv.org/pdf/1206.6844