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Dynamic uncertain causality graph based on cloud model theory for knowledge representation and reasoning

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Abstract

The dynamic uncertain causality graph (DUCG), which has been widely applied in many fields, is an important modelling technique for knowledge representation and reasoning. However, the extant DUCG models have been criticized because they cannot precisely represent experts’ knowledge owing to the ignorance of the fuzziness and randomness of uncertain knowledge. In response, we propose a new type of DUCG model called the cloud reasoning dynamic uncertain causality graph (CDUCG). The CDUCG model, which is based on cloud model theory, can handle with the fuzziness and randomness of uncertain information simultaneously. Moreover, an inference algorithm based on the combination of CDUCG and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to implement fuzzy knowledge inference effectively and thus make the expert systems more dependable and intelligent. Finally, illustrative examples and an industrial application concerning root cause analysis of aluminum electrolysis are provided to demonstrate the proposed CDUCG model. And experimental results show that the new CDUCG model is flexible and reliable for knowledge representation and reasoning.

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Acknowledgements

This project is partly supported by the National Natural Science Foundation of China (Grant Nos. 61725306, 61751312, 61773405 and 61533020) and the Fundamental Research Funds for the Central Universities of Central South University (2019zzts063).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Li Li, Yongfang Xie, Xiaofang Chen & Zhaohui Zeng

  2. Peng Cheng Laboratory, Shenzhen, 518000, China

    Li Li

  3. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Weichao Yue

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  1. Li Li
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  2. Yongfang Xie
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  3. Xiaofang Chen
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  5. Zhaohui Zeng
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Correspondence to Xiaofang Chen.

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Li, L., Xie, Y., Chen, X. et al. Dynamic uncertain causality graph based on cloud model theory for knowledge representation and reasoning. Int. J. Mach. Learn. & Cyber. 11, 1781–1799 (2020). https://doi.org/10.1007/s13042-020-01072-z

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