Journal of Computer Science and Technology

, Volume 15, Issue 3, pp 287–294 | Cite as

A causal model for diagnostic reasoning

  • Peng Guoqiang Email author
  • Cheng Hu 


Up to now, there have been many methods for knowledge representation and reasoning in causal networks, but few of them include the research on the coactions of nodes. In practice, ignoring these coactions may influence the accuracy of reasoning and even give rise to incorrect reasoning. In this paper, based on multilayer causal networks, the definitions on coaction nodes are given to construct a new causal network called coaction Causal Network, which serves to construct a model of neural network for diagnosis followed by fuzzy reasoning, and then the activation rules are given and neural computing methods are used to finish the diagnostic reasoning. These methods are proved in theory and a method of computing the number of solutions for the diagnostic reasoning is given. Finally, the experiments and the conclusions are presented.


coaction node coaction causal network neural network fuzzy reasoning diagnostic reasoning causal network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Pearl J. Distributed revision of belief commitment in multi-hypotheses interpretation. InProc. the 2nd AAAI Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA, Aug. 1986, pp. 201–209.Google Scholar
  2. [2]
    Pearl J. Fusion, propagation and structuring in belief networks.Artif. Intell., 1986, 29: 241–288.zbMATHCrossRefMathSciNetGoogle Scholar
  3. [3]
    Pearl J. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988.Google Scholar
  4. [4]
    Peng Yun, Reggia J A. Abductive Inference Models for Diagnostic Problem-Solving. New York: Springer-Verlag, 1990.zbMATHGoogle Scholar
  5. [5]
    Xu Yue, Cheng Hu. An approach to integrated diagnosis systems. InProc. International Conference on Data and Knowledge Systems for Manufacturing and Engineering, Chinese University of Hong Kong, May 2–4, 1994.Google Scholar
  6. [6]
    Peng Guoqiang, Cheng Hu. Adding cooperation nodes in causal networks. InThe Fourth World Congress on Expert System, Mexico City, March 16–20, 1998, 2: 684–691.Google Scholar
  7. [7]
    Peng Yun, Zhou Zonglin. A neural network learning method for belief networks.International Journal of Intelligent Systems, 1996, 11: 893–915.CrossRefGoogle Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2000

Authors and Affiliations

  1. 1.Institute of SoftwareChinese Academy of SciencesBeijingP.R. China

Personalised recommendations