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Balancing Performance and Human Autonomy With Implicit Guidance Agent.

Ryo Nakahashi1, Seiji Yamada1,2

  • 1Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies(SOKENDAI), Chiyoda, Japan.

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|October 8, 2021
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Summary

This study explores implicit guidance in human-AI collaboration. Agents subtly guide humans to improve plans, balancing task effectiveness with user autonomy.

Keywords:
POMDPcollaborative agenthuman autonomyhuman-agent interactiontheory of mind

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Area of Science:

  • Human-AI Collaboration
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Human-agent teaming requires effective collaboration for task completion.
  • Cognitive limitations can hinder human planning in complex tasks.
  • Explicit agent guidance may reduce human autonomy.

Purpose of the Study:

  • Investigate implicit guidance strategies for human-AI collaboration.
  • Develop an agent that supports human planning without compromising autonomy.
  • Evaluate the effectiveness of implicit guidance in balancing plan improvement and autonomy.

Main Methods:

  • Integrated Bayesian Theory of Mind into collaborative planning algorithms.
  • Developed a collaborative agent employing implicit guidance.
  • Conducted a behavioral experiment to assess human-agent interaction.

Main Results:

  • Implicit guidance effectively supports human plan improvement.
  • Humans maintained a sense of autonomy while enhancing task plans.
  • The developed agent successfully balanced guidance and autonomy.

Conclusions:

  • Implicit guidance is a viable strategy for effective human-AI collaboration.
  • Maintaining human autonomy is crucial for successful human-agent teaming.
  • Bayesian Theory of Mind integration enhances collaborative agent capabilities.