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

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.