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对一阶知识编译的完整性概率推理

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference
课程网址: http://videolectures.net/nips2011_broeck_inference/  
主讲教师: Guy Van den Broeck
开课单位: 加州大学洛杉矶分校
开课时间: 2012-01-25
课程语种: 英语
中文简介:
概率逻辑由于其对知识表示和学习的表达能力,在今天受到了广泛的关注。然而,当在命题层次上进行推理时,这种表达方式不利于推理的可驾驭性。为了解决这一问题,提出了各种提升推理算法,将一阶推理的对象群作为一个整体。尽管存在各种提升推理方法,但这些算法目前没有完整性结果。本文的主要贡献在于,我们引入了提升推理的形式定义,使我们能够对提升推理算法相对于特定类别概率模型的完整性进行推理。然后,我们展示了如何使用一阶知识编译方法获得包含最多两个逻辑变量的公式理论的完整性结果。
课程简介: Probabilistic logics are receiving a lot of attention today because of their expressive power for knowledge representation and learning. However, this expressivity is detrimental to the tractability of inference, when done at the propositional level. To solve this problem, various lifted inference algorithms have been proposed that reason at the first-order level, about groups of objects as a whole. Despite the existence of various lifted inference approaches, there are currently no completeness results about these algorithms. The key contribution of this paper is that we introduce a formal definition of lifted inference that allows us to reason about the completeness of lifted inference algorithms relative to a particular class of probabilistic models. We then show how to obtain a completeness result using a first-order knowledge compilation approach for theories of formulae containing up to two logical variables.
关 键 词: 概率逻辑; 推理算法; 一阶知识; 知识汇编方法
课程来源: 视频讲座网
最后编审: 2020-06-10:liush
阅读次数: 65