Uncertainty modelling in multi-agent information fusion systems

Weng, J; Xiao, F; Cao, Z

Uncertainty modelling in multi-agent information fusion systems

Weng, J Xiao, F Cao, Z

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Publication Type:: Conference Proceeding
Citation:: Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), 2020, 2020-May, pp. 1494-1502
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Weng, J
dc.contributor.author	Xiao, F
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date	2020-05-09
dc.date.accessioned	2021-06-04T06:54:19Z
dc.date.available	2021-06-04T06:54:19Z
dc.date.issued	2020-01-01
dc.identifier.citation	Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), 2020, 2020-May, pp. 1494-1502
dc.identifier.isbn	9781450375184
dc.identifier.issn	1548-8403
dc.identifier.issn	1558-2914
dc.identifier.uri	http://hdl.handle.net/10453/149420
dc.description.abstract	In the field of informed decision-making, the usage of a single diagnostic expert system has limitations when dealing with complicated circumstances. The usage of a multi-agent information fusion (MAIF) system can mitigate this situation, as it allows multiple agents collaborating 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. Target to 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.
dc.language	en
dc.relation.ispartof	Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)
dc.relation.ispartof	International Conference on Autonomous Agents and Multiagent Systems
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Uncertainty modelling in multi-agent information fusion systems
dc.type	Conference Proceeding
utslib.citation.volume	2020-May
utslib.location.activity	New Zealand
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-06-04T06:54:18Z
pubs.finish-date	2020-05-13
pubs.publication-status	Published
pubs.start-date	2020-05-09
pubs.volume	2020-May

Abstract:

In the field of informed decision-making, the usage of a single diagnostic expert system has limitations when dealing with complicated circumstances. The usage of a multi-agent information fusion (MAIF) system can mitigate this situation, as it allows multiple agents collaborating 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. Target to 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.

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http://hdl.handle.net/10453/149420