Paper Info
Title
Uncertainty Modelling in Multi-agent Information Fusion Systems
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.
Year
DOI
Venue
2020
10.5555/3398761.3398933
AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems Auckland New Zealand May, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7518-4
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jiali Weng100.34
Fuyuan Xiao220119.11
Ze-Hong Cao39615.40