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Uncertainty modelling in multi-agent information fusion systems
Citation
Weng, J and Xiao, F and Cao, Z, Uncertainty modelling in multi-agent information fusion systems, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), 9-13 May 2020, University of Auckland (virtual/online), pp. 1494-1502. (2020) [Refereed Conference Paper]
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Copyright Statement
Copyright 2020 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org)
Official URL: http://www.ifaamas.org/Proceedings/aamas2020/forms...
Abstract
In the field of informed decision-making, the usage of a single diagnostic expert system has limitations when dealing with complex circumstances. The usage of a multi-agent information fusion (MAIF) system can mitigate this situation, as it allows multiple agents collaborating together to solve the problems in a complex environment. However, the MAIF system needs to handle the uncertainty problem between different agents objectively at the same time. Aiming at this goal, this study reconstructs the generation of basic probability assignments (BPAs) based on the framework of evidence theory and presents the uncertainty relationship between recognition sets, which are beneficial to the applications of
the MAIF system. On the basis of evidence distance measurement, our method demonstrates the effectiveness and extendibility in numerical examples, and improves the accuracy and anti-interference ability during the identification process in the MAIF system.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | uncertainty modelling, evidence theory, uncertainty, multi-agent information fusion, reconstructed BPA |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Defence |
Objective Group: | Defence |
Objective Field: | Intelligence, surveillance and space |
UTAS Author: | Cao, Z (Dr Zehong Cao) |
ID Code: | 137309 |
Year Published: | 2020 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2020-02-09 |
Last Modified: | 2020-10-27 |
Downloads: | 0 |
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