The Need for Fuzzy AI

作     者：Jonathan M.Garibaldi 

作者机构：IEEE the School of Computer Science University of Nottingham 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2019年第6卷第3期

页      面：610-622页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：supported by University of Nottingham 

主　　题：Artificial intelligence approximate reasoning fuzzy inference systems fuzzy sets human reasoning 

摘      要：Artificial intelligence(AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by *** systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is *** paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including *** conclusion, this paper argues for the need for fuzzy AI in two senses:(i) the need for fuzzy methodologies(in the technical sense of Zadeh s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and(ii) the need for fuzziness(in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.