Dynamic Human Trust Modeling of Autonomous Agents With Varying Capability and Strategy
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2024
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Abstract
Objective We model the dynamic trust of human subjects in a
human-autonomy-teaming screen-based task.
Background Trust is an emerging area of study in human-robot collaboration.
Many studies have looked at the issue of robot performance as a sole predictor
of human trust, but this could underestimate the complexity of the interaction.
Method Subjects were paired with autonomous agents to search an on-screen
grid to determine the number of outlier objects. In each trial, a different
autonomous agent with a preassigned capability used one of three search
strategies and then reported the number of outliers it found as a fraction of
its capability. Then, the subject reported their total outlier estimate. Human
subjects then evaluated statements about the agent's behavior, reliability, and
their trust in the agent.
Results 80 subjects were recruited. Self-reported trust was modeled using
Ordinary Least Squares, but the group that interacted with varying capability
agents on a short time order produced a better performing ARIMAX model. Models
were cross-validated between groups and found a moderate improvement in the
next trial trust prediction.
Conclusion A time series modeling approach reveals the effects of temporal
ordering of agent performance on estimated trust. Recency bias may affect how
subjects weigh the contribution of strategy or capability to trust.
Understanding the connections between agent behavior, agent performance, and
human trust is crucial to improving human-robot collaborative tasks.
Application The modeling approach in this study demonstrates the need to
represent autonomous agent characteristics over time to capture changes in
human trust.
| Reference Key |
joshi2024dynamic
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|---|---|
| Authors | Jason Dekarske; Zhaodan Kong; Sanjay Joshi |
| Journal | arXiv |
| Year | 2024 |
| DOI |
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