Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment

A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as safety assessment, medical diagnosis, fault diagnosis. However, the convention DUCG fails to experts’ precisely because parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend use linguistic terms express their judgements due professional limitations information deficiency. To overcome shortcomings of DUCGs, this article proposes new type by integrating Pythagorean sets (PULSs) evaluation based on distance from average solution (EDAS) method. particular, form PULSs, can depict uncertainty vagueness expert knowledge. Furthermore, gathers evaluations handles conflicting opinions among them. Moreover, reasoning algorithm EDAS method proposed improve reliability intelligence systems. Lastly, an industrial example concerning root cause analysis abnormal aluminum electrolysis cell condition provided demonstrate model.