Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems

Lin, CT; Fan, HY; Chang, YC; Ou, L; Liu, J; Wang, YK; Jung, TP

Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems

Lin, CT Fan, HY Chang, YC Ou, L Liu, J Wang, YK Jung, TP

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Technologies, 2022, 10, (6), pp. 115
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Lin, CT
dc.contributor.author	Fan, HY
dc.contributor.author	Chang, YC
dc.contributor.author	Ou, L
dc.contributor.author	Liu, J
dc.contributor.author	Wang, YK
dc.contributor.author	Jung, TP
dc.date.accessioned	2023-03-03T04:42:44Z
dc.date.available	2023-03-03T04:42:44Z
dc.date.issued	2022-12-01
dc.identifier.citation	Technologies, 2022, 10, (6), pp. 115
dc.identifier.issn	2227-7080
dc.identifier.issn	2227-7080
dc.identifier.uri	http://hdl.handle.net/10453/166650
dc.description.abstract	The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over (Formula presented.) compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
dc.language	en
dc.publisher	MDPI
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation.ispartof	Technologies
dc.relation.isbasedon	10.3390/technologies10060115
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems
dc.type	Journal Article
utslib.citation.volume	10
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	open_access	*
dc.date.updated	2023-03-03T04:42:41Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	6

Abstract:

The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over (Formula presented.) compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.

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http://hdl.handle.net/10453/166650