智能科学与技术学报 ›› 2019, Vol. 1 ›› Issue (4): 319-326.doi: 10.11959/j.issn.2096-6652.201936

• 学术论文 •    下一篇

差异与学习:模糊系统与模糊推理

GARIBALDI Jonathan M1,陈虹宇2(),李小双2   

  1. 1 诺丁汉大学计算机科学学院,英国 诺丁汉郡 NG8 1BB
    2 中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京 100190
  • 修回日期:2019-11-22 出版日期:2019-12-20 发布日期:2020-02-29
  • 作者简介:GARIBALDI Jonathan M(1963- ),男,博士,英国诺丁汉大学计算机科学学院院长,主要研究方向为人类推理的不确定性和差异建模、复杂数据建模和解释|陈虹宇(1994- ),女,中国科学院自动化研究所复杂系统管理与控制国家重点实验室硕士生,主要研究方向为交通数据分析、社会交通、智能交通|李小双(1995- ),男,中国科学院自动化研究所复杂系统管理与控制国家重点实验室硕士生,主要研究方向为智能交通系统、强化学习

Variation and learning:fuzzy system and fuzzy inference

GARIBALDI Jonathan M1,Hongyu CHEN2(),Xiaoshuang LI2   

  1. 1 School of Computer Science,University of Nottingham,Nottinghamshire NG8 1BB,UK
    2 The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Revised:2019-11-22 Online:2019-12-20 Published:2020-02-29

摘要:

作为一种决策支持系统,模糊系统不仅具有处理不确定性信息的能力,又能够明确表达不确定性知识和推理过程。但现存的一个问题是,对于包括采用模糊方法的系统在内的计算机决策支持系统,目前还未出现能够明确评估系统实际可行性的方法。提出了不可区分性的概念框架,并将其作为评估计算机决策支持系统的关键部分,给出了相关案例研究。案例证明人类专家的评判并非完美,模糊系统能够在技术层面模拟人类的决策,包括人类专家在评判时表现出的差异性。使用模糊方法进行基于知识不确定性的表达与推理是非常必要的,而差异则是学习时不可避免的表现形式,在评估人工智能系统时应接受其不完美的决策。

关键词: 人工智能, 近似推理, 模糊推理系统, 模糊集合, 人类推理

Abstract:

As a decision support system,fuzzy system can deal with uncertainty and has a clear representation of uncertainty knowledge and inference process.But one problem that exists is that computerized decision support systems,including systems that use fuzzy methods,do not have a clear assessment method to determine whether they can be allowed to be used in the real world.A conceptual framework of indistinguishable lines as a key component in evaluating computerized decision support systems was proposed,and some case studies were given.The case proves that the performance of human experts is not perfect,and the fuzzy system can simulate human performance at the technical level,including the variation of human experts.In summary,fuzzy methods are necessary for the representation and reasoning of uncertainty of the knowledge-based systems.Variation is an important form of learning.When evaluating AI systems,imperfect performance should be accepted.

Key words: artificial intelligence, approximate reasoning, uzzy inference system, fuzzy set, human reasoning

中图分类号: 

No Suggested Reading articles found!