内容理解中的信息因果关系和不确定性

A. Raglin, Raha Moraffah, Huan Liu
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引用次数: 0

摘要

任务需要对环境或构成环境的背景有一个清晰的认识。此外,任务需要清楚地理解内容,以及将用于完成任务的可用信息。通常,这项任务涉及一个或一系列决策。然而,获得这些内容并不完美。理解内容中可能存在的约束、限制和不确定性增加了挑战。为了试图产生和表达这一想法的信息的不确定性概念,其中包括因果推理的关键方面,在本文中提出。本文讨论了信息不确定性(UoI)的概念,以及如何将因果关系注入到这一概念中,从而不仅仅为不确定性作为原因提供另一种价值。此外,因果ui概念是否可以扩展这个想法,以便计算表达式可以捕捉因果推理的细微差别?本文提出了一种可能的设想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causality and Uncertainty of Information for Content Understanding
Tasks require a clear picture of the context or the backdrop that frames the circumstances. Additionally tasks require a clear understanding of the content, the information available that will be used for completion of the task. Often the task involves a single or a set of decisions along the way. However, obtaining that content is not a perfect one. Understanding the content with is possible constraints, limitations, uncertainties adds to the challenge. To attempt to generate and express this the idea of an uncertainty of information concept that includes key aspects of causal reasoning is presented in this paper. In the paper the uncertainty of information (UoI) idea is discussed and how causality can be infused into this concept to not just provide another value for uncertainty be the causes. Moreover, can a causal UoI concept expand the idea so that a computational expression can capture the nuances of causal reasoning? This paper presents a possible vision.
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