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Intelligent Agent Transparency in Human-Agent Teaming for Multi-UxV Management.

Joseph E Mercado1, Michael A Rupp2, Jessie Y C Chen3

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Increased agent transparency in human-agent teaming improved operator performance and trust without increasing workload. This research supports the benefits of transparency for effective multirobot management in military settings.

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human–agent teamingintelligent agent transparencymulti-UxV management

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Area of Science:

  • Human-computer interaction
  • Human-robot interaction
  • Human-agent teaming

Background:

  • Investigated operator performance, trust, and workload in multirobot management.
  • Participants acted as unmanned vehicle (UxV) operators, guided by an intelligent agent (IA).
  • The IA provided plan recommendations for mission completion.

Purpose of the Study:

  • To examine the impact of varying levels of agent transparency on operator performance, trust, and workload.
  • To assess the effectiveness of intelligent agent recommendations in a multirobot management context.

Main Methods:

  • A within-subjects design with three levels of agent transparency.
  • Eight missions per block, with IA errors introduced.
  • Collected data on operator performance, trust, workload, and usability.

Main Results:

  • Operator performance, trust, and perceived usability significantly increased with higher transparency levels.
  • No significant increase in subjective or objective workload was observed.
  • Response times remained consistent across different transparency levels.

Conclusions:

  • Transparency enhances operator performance and trust without imposing additional workload or response time costs.
  • Findings support the implementation of intelligent agents in military settings for heterogeneous unmanned vehicle (UxV) team design.