The Influence of Interdependence on Trust Repair in Human-Agent Teams

Comparing the Effectiveness of Trust Repair Strategies in Full Independence and Complementary Independence

Bachelor Thesis (2023)
Author(s)

C. Kim (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Ruben S. Verhagen – Mentor (TU Delft - Interactive Intelligence)

Myrthe L. Tielman – Mentor (TU Delft - Interactive Intelligence)

Ujwal Gadiraju – Graduation committee member (TU Delft - Web Information Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Cherin Kim
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Cherin Kim
Graduation Date
03-07-2023
Awarding Institution
Delft University of Technology
Project
['CSE3000 Research Project']
Programme
['Computer Science and Engineering']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

As autonomous systems are increasingly integrated as a team member for collaborative tasks, trust in human-agent teams (HAT) becomes crucial to foster success. In many real world scenarios, trust violations are expected, thus demanding the use of trust repair strategies to restore damaged trust. Previous research has shown that expressing regret and providing explanations are effective strategies to rebuild human-agent trust.
However, the role of various team dynamics, such as interdependence relationships, remains unexplored. The aim of this paper is to examine the influence of interdependence levels on the effectiveness of trust repair strategies. To investigate this, an experiment was conducted in a collaboration environment with an urban search and rescue mission. Two interdependence conditions were introduced to analyse their effect on trust and collaboration fluency.
No significant evidence was found to support a relationship between interdependence and trust repair or collaboration fluency. However, as this study only considers two interdependence conditions, there is much more room for future work to explore further. This study can bring meaningful insights to design and facilitate agents that are more trustworthy in human-agent collaboration settings.

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