New Linguistic Z-Number Petri Nets for Knowledge Acquisition and Representation Under Large Group Environment

In this paper, we develop a new model named linguistic Z-number Petri nets for knowledge acquisition and representation in the large group environment. First, the linguistic Z-number production rules are introduced for knowledge representation, where truth degrees, threshold values, and certainty fa...

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Published inInternational journal of fuzzy systems Vol. 24; no. 8; pp. 3483 - 3500
Main Authors Shi, Hua, Liu, Hu-Chen, Wang, Jing-Hui, Mou, Xun
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022
Springer Nature B.V
Subjects
Artificial Intelligence
Cognition & reasoning
Complexity
Computational Intelligence
Decision making
Engineering
Fuzzy sets
Knowledge acquisition
Knowledge representation
Linguistics
Management Science
Methods
Operations Research
Petri nets
Risk assessment
Risk levels
Security
Sensitivity analysis
Subject specialists
Linguistic Z-number
Knowledge acquisition
Expert system
Knowledge representation
Linguistic Petri net
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Summary:In this paper, we develop a new model named linguistic Z-number Petri nets for knowledge acquisition and representation in the large group environment. First, the linguistic Z-number production rules are introduced for knowledge representation, where truth degrees, threshold values, and certainty factors are described by linguistic Z-numbers. Subsequently, a knowledge acquisition approach is put forward to obtain the knowledge parameters of linguistic Z-number Petri nets based on a large group of experts. To reduce the complexity of knowledge reasoning, a simplification method is proposed to simplify the structure of linguistic Z-number Petri nets. Finally, a real case of chemical security risk assessment is provided to demonstrate the practicability and effectiveness of the proposed linguistic Z-number Petri net model. The results show that risk level of the given chemical plant is high and efficient actions should be taken to identify threat drivers and reduce security risk. Moreover, through a sensitivity analysis and a comparative analysis, it is concluded that the proposed linguistic Z-number Petri nets can represent experts’ complex and uncertain cognitive information comprehensively and acquire more precise and reasonable knowledge from domain experts.
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ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-022-01341-9