## 2 Literature Review  
In recent years, some extended DUCGs have been proposed to compensate for the shortcomings of existing methods. For example, [PERSON] et al. [9] extended the DUCG to include the representation and inference algorithm for non-causal classification relationships. [PERSON] et al. [29] proposed an extended DUCG methodology to intuitively represent complex medical knowledge and perform effective clinical diagnose. [PERSON] et al. [13] provided a DUCG model based on multiple conditional events and weighted graphs for shale-gas sweet-spot evaluation. [PERSON] and [PERSON] [30] presented a multi-valued DUCG method to calculate the joint probability distribution of the directed cycle graph with local data and domain causal knowledge. [PERSON] et al. [3] reported an artificial intelligence diagnostic model based on DUCG for improving the efficiency of differential dyspnea diagnosis. [PERSON] and [PERSON] [11] proposed a cubic DUCG methodology characterized by causal connections and dynamic negative feedback loops for temporal process modeling and diagnostic logic inference. [PERSON] et al. [31] presented a hybrid DUCG to reduce the misdiagnosis caused by outpatient triage error and help triage nurses improve their triage accuracy.  
On the one hand, many uncertainty theories have been incorporated into DUCGs to represent the experience and knowledge of experts. For instance, [PERSON] et al. [22] proposed a DUCG based on picture fuzzy sets for uncertain knowledge representation and reasoning in root cause diagnosis. [PERSON] and [PERSON] [20] introduced a DUCG model for root cause analysis, in which the intuitionistic fuzzy sets were used for describing the uncertain event. [PERSON] et al. [21] developed a cloud reasoning dynamic DUCG model, in which the cloud model theory was employed to handle the fuzziness and randomness of uncertain information simultaneously. Combining a fuzzy decision tree with the DUCG, [PERSON] et al. [32] proposed a simplified DUCG model for fault diagnosis in nuclear power plants.  
Furthermore, many researchers have introduced different knowledge inference algorithms for DUCGs. [PERSON] and [PERSON] [33] suggested an inference algorithm of DUCG based on conditional stochastic simulation for complex cases with many state-unknown intermediate variables. [PERSON] and [PERSON] [14] integrated DUCG with event trees to perform probabilistic safety assessments by considering the problems of dependencies and circular loops. In [20,21], the technique of order preference similarity to the ideal solution (TOPSIS) method was utilized to implement fuzzy knowledge inference in DUCG. In [22], an enhanced knowledge reasoning algorithm based on the picture fuzzy operators was developed to resolve causal inference problems. [PERSON] et al. [34] provided a methodology for modeling and reasoning about complex faults with negative feedback with cubic DUCG. [PERSON] et al. [35] proposed a diagnostic modeling and reasoning system using DUCG for the intelligent diagnosis of jaundice, considering the causal interactions among diseases and symptoms.  
Based on the above literature review, it can be seen that many upgraded DUCGs have been developed for KRR. Nevertheless, there is no research that combines PULS s with the EDAS method in DUCG. Additionally, most studies lack the capacity to deal with the conflict judgements of domain experts on knowledge parameters. Hence, this article aims to present a new type of DUCG that combine PULSs and the EDAS method for KRR. In addition, a preference modifying-based method is used to detect and eliminate the conflict among experts to obtain accurate knowledge parameters in the knowledge acquisition process, which is an important issue that received little attention in the literature.