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 probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model exper...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4670 |
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| author | Yu-Jie Zhu Wei Guo Hu-Chen Liu |
| author_facet | Yu-Jie Zhu Wei Guo Hu-Chen Liu |
| author_sort | Yu-Jie Zhu |
| collection | DOAJ |
| description | 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 experts’ knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model. |
| first_indexed | 2024-03-10T04:20:03Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-10T04:20:03Z |
| publishDate | 2022-05-01 |
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| spelling | doaj.art-78415ad9af554df7814fc70c055909c52023-11-23T07:52:17ZengMDPI AGApplied Sciences2076-34172022-05-01129467010.3390/app12094670Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic EnvironmentYu-Jie Zhu0Wei Guo1Hu-Chen Liu2School of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Economics and Management, Tongji University, Shanghai 200092, ChinaA 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 experts’ knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model.https://www.mdpi.com/2076-3417/12/9/4670expert systemknowledge representation and reasoningdynamic uncertain causality graph (DUCG)Pythagorean uncertain linguistic setevaluation based on distance from average solution (EDAS) |
| spellingShingle | Yu-Jie Zhu Wei Guo Hu-Chen Liu Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment Applied Sciences expert system knowledge representation and reasoning dynamic uncertain causality graph (DUCG) Pythagorean uncertain linguistic set evaluation based on distance from average solution (EDAS) |
| title | Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment |
| title_full | Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment |
| title_fullStr | Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment |
| title_full_unstemmed | Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment |
| title_short | Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment |
| title_sort | knowledge representation and reasoning with an extended dynamic uncertain causality graph under the pythagorean uncertain linguistic environment |
| topic | expert system knowledge representation and reasoning dynamic uncertain causality graph (DUCG) Pythagorean uncertain linguistic set evaluation based on distance from average solution (EDAS) |
| url | https://www.mdpi.com/2076-3417/12/9/4670 |
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