Langages et Systemes Informatiques, Université Paul Sabatier, Toulouse Cedex France.
IEEE Trans Pattern Anal Mach Intell. 1985 Mar;7(3):260-83. doi: 10.1109/tpami.1985.4767656.
The intended purpose of this paper is twofold: proposing a common basis for the modeling of uncertainty and imprecision, and discussing various kinds of approximate and plausible reasoning schemes in this framework. Together with probability, different kinds of uncertainty measures (credibility and plausibility functions in the sense of Shafer, possibility measures in the sense of Zadeh and the dual measures of necessity, Sugeno's g¿-fuzzy measures) are introduced in a unified way. The modeling of imprecision in terms of possibility distribution is then presented, and related questions such as the measure of the uncertainty of fuzzy events, the probability and possibility qualification of statements, the concept of a degree of truth, and the truth qualification of propositions, are discussed at length. Deductive inference from premises weighted by different kinds of measures by uncertainty, or by truth-values in the framework of various multivalued logics, is fully investigated. Then, deductive inferences from imprecise or fuzzy premises are dealt with; patterns of reasoning where both uncertainty and imprecision are present are also addressed. The last section is devoted to the combination of uncertain or imprecise pieces of information given by different sources. On the whole, this paper is a tentative survey of quantitative approaches in the modeling of uncertainty and imprecision including recent theoretical proposals as well as more empirical techniques such as the ones developed in expert systems such as MYCIN or PROSPECTOR, the management of uncertainty and imprecision in reasoning patterns being a key issue in artificial intelligence.
提出一种用于对不确定性和不精确性建模的通用基础，并在该框架中讨论各种近似合理的推理方案。与概率一起，不同种类的不确定性度量（Shafer 意义下的可信度和似然度函数、Zadeh 意义下的可能性测度以及必要性的对偶测度、Sugeno 的 g¿-模糊测度）以统一的方式引入。然后提出了基于可能性分布的不精确性建模，并详细讨论了模糊事件的不确定性度量、语句的概率和可能性定性、真理度的概念以及命题的真定性等相关问题。充分研究了用不同种类的不确定性度量（或各种多值逻辑框架中的真值）加权前提进行演绎推理的问题。然后，处理了来自不精确或模糊前提的演绎推理；还涉及了同时存在不确定性和不精确性的推理模式。最后一节专门讨论了来自不同来源的不确定或不精确的信息的组合。总的来说，本文是对不确定性和不精确性建模的定量方法的初步调查，包括最近的理论建议以及更多的经验技术，如 MYCIN 或 PROSPECTOR 等专家系统中开发的技术，不确定性和不精确性在推理模式中的管理是人工智能的关键问题。
