Balancing Performance and Human Autonomy with Implicit Guidance Agent
  The human-agent team, which is a problem in which humans and autonomous
agents collaborate to achieve one task, is typical in human-AI collaboration.
For effective collaboration, humans want to have an effective plan, but in
realistic situations, they might have difficulty calculating the best plan due
to cognitive limitations. In this case, guidance from an agent that has many
computational resources may be useful. However, if an agent guides the human
behavior explicitly, the human may feel that they have lost autonomy and are
being controlled by the agent. We therefore investigated implicit guidance
offered by means of an agent's behavior. With this type of guidance, the agent
acts in a way that makes it easy for the human to find an effective plan for a
collaborative task, and the human can then improve the plan. Since the human
improves their plan voluntarily, he or she maintains autonomy. We modeled a
collaborative agent with implicit guidance by integrating the Bayesian Theory
of Mind into existing collaborative-planning algorithms and demonstrated
through a behavioral experiment that implicit guidance is effective for
enabling humans to maintain a balance between improving their plans and
retaining autonomy.
