Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance
The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/7/820 |
| _version_ | 1797528601521815552 |
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| author | Jingyu Liu Yongchuan Tang |
| author_facet | Jingyu Liu Yongchuan Tang |
| author_sort | Jingyu Liu |
| collection | DOAJ |
| description | The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system. |
| first_indexed | 2024-03-10T10:00:44Z |
| format | Article |
| id | doaj.art-8234e827b15144f483cd248ba5563858 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T10:00:44Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-8234e827b15144f483cd248ba55638582023-11-22T01:58:20ZengMDPI AGEntropy1099-43002021-06-0123782010.3390/e23070820Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence DistanceJingyu Liu0Yongchuan Tang1School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, ChinaSchool of Big Data and Software Engineering, Chongqing University, Chongqing 401331, ChinaThe multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.https://www.mdpi.com/1099-4300/23/7/820Dempster–Shafer evidence theoryuncertaintymulti-agent information fusionbase basic probability assignment |
| spellingShingle | Jingyu Liu Yongchuan Tang Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance Entropy Dempster–Shafer evidence theory uncertainty multi-agent information fusion base basic probability assignment |
| title | Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance |
| title_full | Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance |
| title_fullStr | Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance |
| title_full_unstemmed | Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance |
| title_short | Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance |
| title_sort | conflict data fusion in a multi agent system premised on the base basic probability assignment and evidence distance |
| topic | Dempster–Shafer evidence theory uncertainty multi-agent information fusion base basic probability assignment |
| url | https://www.mdpi.com/1099-4300/23/7/820 |
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