Balancing Performance and Human Autonomy With Implicit Guidance Agent

Abstract

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.

Beyond AI-powered context-aware services: the role of human–AI collaboration

“…Recent advancements in AI could make it possible for people and technology to work together and integrate seamlessly (Korteling et al, 2021) Human-AI collaboration for AI-powered services (Andrews et al, 2022;Nakahashi and Yamada, 2021;Peeters et al, 2021). Metaphors such as "teammates," "partners" and "collaborators" in related studies highlighted a high degree of cooperation, complementarity and equality for human-AI collaboration (Korteling et al, 2021).…”

“…As a result, more research has started to investigate the potential for AI and humans to work together in collaborative settings (Andrews et al. , 2022; Nakahashi and Yamada, 2021; Peeters et al. , 2021).…”