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Uncertainty modelling in multi-agent information fusion systems

conference contribution
posted on 2023-05-23, 14:28 authored by Weng, J, Xiao, F, Cao, Z
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.

History

Publication title

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)

Editors

B An, N Yorke-Smith, A El Fallah Seghrouchni and G Sukthankar

Pagination

1494-1502

ISSN

2523-5699

Department/School

School of Information and Communication Technology

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Place of publication

Online

Event title

International Conference on Autonomous Agents and Multiagent Systems 2020

Event Venue

University of Auckland (virtual/online)

Date of Event (Start Date)

2020-05-09

Date of Event (End Date)

2020-05-13

Rights statement

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

Repository Status

  • Restricted

Socio-economic Objectives

Intelligence, surveillance and space

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    University Of Tasmania

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