Human-Autonomous Teaming Framework Based on Trust Modelling

Ma, W; Chang, YC; Wang, YK; Lin, CT

Human-Autonomous Teaming Framework Based on Trust Modelling

Ma, W Chang, YC Wang, YK Lin, CT

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13728 LNAI, pp. 707-718
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ma, W
dc.contributor.author	Chang, YC
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date.accessioned	2023-03-23T04:22:28Z
dc.date.available	2023-03-23T04:22:28Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13728 LNAI, pp. 707-718
dc.identifier.isbn	9783031226946
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/168196
dc.description.abstract	With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.
dc.language	en
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation	Lockheed Martin Corporation
dc.relation	Defence Science and Technology Group of the Department of DefenceDSTG
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-031-22695-3_49
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Human-Autonomous Teaming Framework Based on Trust Modelling
dc.type	Conference Proceeding
utslib.citation.volume	13728 LNAI
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	closed_access	*
dc.date.updated	2023-03-23T04:22:27Z
pubs.publication-status	Published
pubs.volume	13728 LNAI

Abstract:

With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.

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http://hdl.handle.net/10453/168196