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Uncertainty modelling in multi-agent information fusion systems

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Weng, J, Xiao, F and Cao, Z ORCID: 0000-0003-3656-0328 2020 , 'Uncertainty modelling in multi-agent information fusion systems', paper presented at the International Conference on Autonomous Agents and Multiagent Systems 2020, 9-13 May 2020, University of Auckland (virtual/online).

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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 ofthe 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 Type: Conference or Workshop Item (Paper)
Authors/Creators:Weng, J and Xiao, F and Cao, Z
Keywords: uncertainty modelling, evidence theory, uncertainty, multi-agent information fusion, reconstructed BPA
Journal or Publication Title: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)
Publisher: International Foundation for Autonomous Agents and Multiagent Systems
Copyright Information:

Copyright 2020 International Foundation for AutonomousAgents and Multiagent Systems (www.ifaamas.org)

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