Formalization of Evidence: A Comparative Study

Pei Wang
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引用次数: 20

Abstract

Formalization of Evidence: A Comparative Study This article analyzes and compares several approaches of formalizing the notion of evidence in the context of general-purpose reasoning system. In each of these approaches, the notion of evidence is defined, and the evidence-based degree of belief is represented by a binary value, a number (such as a probability), or two numbers (such as an interval). The binary approaches provide simple ways to represent conclusive evidence, but cannot properly handle inconclusive evidence. The one-number approaches naturally represent inconclusive evidence as a degree of belief, but lack the information needed to revise this degree. It is argued that for systems opening to new evidence, each belief should at least have two numbers attached to indicate its evidential support. A few such approaches are discussed, including the approach used in NARS, which is designed according to the considerations of general-purpose intelligent systems, and provides novel solutions to several traditional problems on evidence.
证据的形式化:一个比较研究
证据的形式化:比较研究本文分析和比较了通用推理系统背景下证据概念形式化的几种方法。在每一种方法中,都定义了证据的概念,并且基于证据的相信程度由一个二值、一个数字(如概率)或两个数字(如区间)表示。二元法提供了表示结论性证据的简单方法,但不能正确处理非结论性证据。一个数字的方法自然代表了不确定的证据作为一个相信的程度,但缺乏必要的信息来修改这个程度。有人认为,对于接受新证据的系统,每个信念至少应该附带两个数字来表明其证据支持。本文讨论了一些这样的方法,包括NARS中使用的方法,该方法是根据通用智能系统的考虑而设计的,并为几个传统的证据问题提供了新的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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