不确定性推理中定性信念的丰富

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摘要翻译：
本文研究了丰富的定性信念函数，用于不确定性推理和通过语言标记组合自然语言表达的信息。本文考虑了语言标记的两种可能的丰富（定量和/或定性），并提出和解释了处理这些丰富的运算符（加法、乘法、除法等）。我们将它们称为$qe$-operators，$qe$代表“定性丰富的”operators。这些算子可以看作是最近在Dezert-Smarandache似是而非推理理论(DSmT)中提出的经典定性算子($q$-算子）的直接推广。$q$-运算符在本文中也有详细说明。语言标记的定量充实是一个数值支持度，在$[0,\infty)$中，而定性充实则取其值于有限有序的语言值集合中。定量充实的精确度不如定性充实，但它与人类专家在表达带有支持度的语言标记时所能提供的结果更接近。给出了两个简单的例子来说明如何进行定性充实信念赋值的融合。
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英文标题：
《Enrichment of Qualitative Beliefs for Reasoning under Uncertainty》
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作者：
Xinde Li (ICRL), Xinhan Huang (ICRL), Florentin Smarandache (UNM),
  Jean Dezert (ONERA)
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最新提交年份：
2007
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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：
  This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrichments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multiplication, division, etc) for dealing with them are proposed and explained. We denote them $qe$-operators, $qe$ standing for "qualitative-enriched" operators. These operators can be seen as a direct extension of the classical qualitative operators ($q$-operators) proposed recently in the Dezert-Smarandache Theory of plausible and paradoxist reasoning (DSmT). $q$-operators are also justified in details in this paper. The quantitative enrichment of linguistic label is a numerical supporting degree in $[0,\infty)$, while the qualitative enrichment takes its values in a finite ordered set of linguistic values. Quantitative enrichment is less precise than qualitative enrichment, but it is expected more close with what human experts can easily provide when expressing linguistic labels with supporting degrees. Two simple examples are given to show how the fusion of qualitative-enriched belief assignments can be done.
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PDF链接：
https://arxiv.org/pdf/0709.1701

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关键词：不确定性 不确定 确定性 Quantitative Presentation 信念 语言 用于 支持 linguistic