• Article
  • | |
  • Metrics
  • |
  • Reference [3]
  • |
  • Related [20]
  • |
  • Cited by [5]
  • | |
  • Comments
    Abstract:

    In this paper a unified framework for representing, reasoning and learning of uncertain information is put forward. The 4-valued recognition structure is used to measure the uncertain degree of uncertain information and thus more powerful expressive capacity is attained. Moreover, the framework supports the efficient acquisition of the uncertain information. These features make the framework more practical than existing theories of reasoning about uncertainty.

    Reference
    [1] Copper,G.F.The computational complexity of probabilistic inference on uncertainty.Artificial Intelligence,1990,42:393~405.
    [2] Koller,D.Probabilistic frame-based system.In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98).1998.580~578.
    [3] Boyen,X.,Friedman,N.,Koller,D.Discover the hidden structure of complex dynamic system.In: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence.1999.91~100.
    Comments
    Comments
    分享到微博
    Submit
Get Citation

刘洁,陈小平,蔡庆生,范焱.不确定信息的认知结构表示、推理和学习.软件学报,2002,13(4):649-651

Copy
Share
Article Metrics
  • Abstract:4089
  • PDF: 4906
  • HTML: 0
  • Cited by: 0
History
  • Received:March 13,2000
  • Revised:August 24,2001
You are the first2038637Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063