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

• Published in: Applied sciences Vol. 12; no. 9; p. 4670
• Main Authors: Zhu, Yu-Jie, Guo, Wei, Liu, Hu-Chen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Causality; dynamic uncertain causality graph (DUCG); evaluation based on distance from average solution (EDAS); expert system; Fault diagnosis; Fuzzy sets; Knowledge acquisition; Knowledge representation; knowledge representation and reasoning; Linguistics; Literature reviews; Methods; Nuclear power plants; Pythagorean uncertain linguistic set; Subject specialists; Variables

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 probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model exper...