摘要翻译：
在这一章中，我们提出了一种新的实用的维恩图元素的编码，以便于操作焦点元素。为了减少复杂性，最终的制约因素必须在开始的编纂中加以整合。因此，我们只考虑一个简化的超幂集$D_R^\theta$，它可以是$2^\theta$或$D^\theta$。我们描述了一个一般信念函数框架的所有步骤。特别研究了判定步骤，当我们可以判定识别空间中单点的交点时，实际的判定函数并不容易使用。因此，提出了两种方法，一种是对前一种方法的扩展，另一种是基于决定所依据的要素的特殊性的方法。本章的主要目的是为需要信念函数理论的研究者和使用者提供一个通用信念函数框架的实用代码。
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英文标题：
《Implementing general belief function framework with a practical
  codification for low complexity》
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作者：
Arnaud Martin (E3I2)
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最新提交年份：
2008
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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：
  In this chapter, we propose a new practical codification of the elements of the Venn diagram in order to easily manipulate the focal elements. In order to reduce the complexity, the eventual constraints must be integrated in the codification at the beginning. Hence, we only consider a reduced hyper power set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the steps of a general belief function framework. The step of decision is particularly studied, indeed, when we can decide on intersections of the singletons of the discernment space no actual decision functions are easily to use. Hence, two approaches are proposed, an extension of previous one and an approach based on the specificity of the elements on which to decide. The principal goal of this chapter is to provide practical codes of a general belief function framework for the researchers and users needing the belief function theory.
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PDF链接：
https://arxiv.org/pdf/0807.3483