Abstract
Each of twenty participants teamed with a learning capable agent to conduct a threat classification task. The agent’s reasoning and learning transparency varied across four scenarios. Access to agent reasoning transparency improved task performance as assessed by percent of correct classifications. Agent learning transparency of inferred knowledge improved task response time and reduced cognitive workload. However, when the human was burdened with directly teaching the agent, task completion time and perceived workload increased dramatically, while satisfaction in task performance decreased. These findings indicate that when teamed with learning capable agents, human performance and workload are best supported when the autonomy can derive its needed information with minimal human input.
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Acknowledgment
The authors wish to thank Jonathan Coole for his input and contributions to this project. The views and conclusions contained in this paper are those of the authors and should not be interpreted as presenting the official policies or position, either expressed or implied, of the U.S. Army Research Laboratory or the U.S. Government.
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Wright, J.L., Lee, J., Schreck, J.A. (2021). Human-Autonomy Teaming with Learning Capable Agents: Performance and Workload Outcomes. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_1
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