Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

Lin, CT; Zhang, H; Ou, L; Chang, YC; Wang, YK

Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

Lin, CT Zhang, H Ou, L Chang, YC Wang, YK

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8, (1), pp. 229-239
Issue Date:: 2024-02-01

Embargoed

	Filename	Description	Size
	Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion.pdf	Accepted version	3.68 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Embargoed
Open Access

This item is currently unavailable due to the publisher's embargo.

The embargo period expires on 1 Feb 2026

Full metadata record

Field	Value	Language
dc.contributor.author	Lin, CT
dc.contributor.author	Zhang, H
dc.contributor.author	Ou, L
dc.contributor.author	Chang, YC
dc.contributor.author	Wang, YK
dc.date.accessioned	2024-03-05T00:52:43Z
dc.date.available	2024-03-05T00:52:43Z
dc.date.issued	2024-02-01
dc.identifier.citation	IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8, (1), pp. 229-239
dc.identifier.issn	2471-285X
dc.identifier.issn	2471-285X
dc.identifier.uri	http://hdl.handle.net/10453/176100
dc.description.abstract	The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation	United States Department of the NavyN629091912058
dc.relation.ispartof	IEEE Transactions on Emerging Topics in Computational Intelligence
dc.relation.isbasedon	10.1109/TETCI.2023.3319253
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.title	Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion
dc.type	Journal Article
utslib.citation.volume	8
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2026-02-01T00:00:00+1000Z
dc.date.updated	2024-03-05T00:52:40Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	1

Abstract:

The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176100