摘要翻译:
一阶变量消除(FOVE)算法允许精确推理直接应用于概率关系模型,并已被证明大大优于标准推理方法在基础命题模型上的应用。然而,FOVE算子可以在有限的条件下应用,这往往迫使人们诉诸命题推理。为了扩展FOVE的适用性,本文提出了两种新的模型转换算子:第一种是联合公式转换,第二种是正差计数转换。这些新的操作允许有效的推理方法直接应用到关系模型上,而现有的有效方法还不能应用到关系模型上。此外,在这些功能的帮助下,我们展示了如何适应FOVE来为不确定情况下的决策提供关系模型上的最大期望效用(MEU)查询的精确解。实验评估表明,我们的算法提供了显着的加速比替代方案。
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英文标题:
《Extended Lifted Inference with Joint Formulas》
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作者:
Udi Apsel, Ronen I. Brafman
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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:
The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto. In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.
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PDF链接:
https://arxiv.org/pdf/1202.3698