Principals of Uncertainty and Information in Expert Systems

专家系统中的不确定性和信息原理

• 批准号: 9015675
• 负责人: George Klir
• 金额: $ 9.5万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 1993-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9015675&HistoricalAwards=false
• 关键词: Principals; Uncertainty; Information; Expert; Systems

The principal objective of this research is to develop adequate principles for dealing with uncertainty in expert systems. The focus is on four relatively new mathematical theories of uncertainty: possibility theory, fuzzy set theory, random set theory, and the Dempster-Shafer theory of evidence. In each of these theories, as in probability theory, well-justified functions that measure uncertainty are now well established. These functions are crucial for the following three principles of uncertainty to be investigated: principles of maximum uncertainty, minimum uncertainty, and uncertainty invariance. According to the principle of maximum uncertainty, conclusions in any form of ampliative reasoning are determined by maximizing relevant uncertainty function subject to constraints that represent information available. The principle of minimum uncertainty is fundamental to a variety of problems involving unavoidable increase of relevant uncertainty, such as simplification or conflict-resolution problems. According to this principle, solutions are determined by minimizing relevant uncertainty function within constraints representing the requirements of each problem. The principle of uncertainty invariance is concerned with information-preserving transformations between distinct theories of uncertainty. This project is developing mathematical and computational foundation of the three principles as well as investigating their practical role in expert systems.

这项研究的主要目的是开发适当的 专家系统中处理不确定性的原则。 的 重点是四个相对较新的数学理论， 不确定性：可能性理论，模糊集理论，随机集 证据理论和Dempster-Shafer证据理论。 的每一个中 这些理论，如概率论， 测量不确定性的功能现在已经很好地建立起来。 这些功能对于以下三项原则至关重要： 待研究的不确定度：最大值原理 不确定性、最小不确定性和不确定性不变性。 根据最大不确定度原理， 任何形式的推理都是由最大化 相关不确定性函数受约束， 代表可用信息。最小原则 不确定性是各种问题的基础， 不可避免地增加了相关的不确定性，例如 简化或解决冲突问题。 根据 根据这一原则，解决方案是通过最小化相关 约束内的不确定性函数， 每个问题的要求。 测不准原理 不变性与信息保持有关 不同不确定性理论之间的转换。 这 该项目正在开发数学和计算基础 三个原则，并探讨其实际 专家系统的作用。