Given an increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g. perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human-AI teaming case where a managing agent is tasked with identifying when to perform a delegation assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, the sensing deficiencies. These contexts provide cases where the manager must learn to attribute capabilities to suitability for decision-making. As such, we demonstrate how a Reinforcement Learning (RL) manager can correct the context-delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation.

翻译：鉴于能够自主行动或增强人类活动的智能系统日益普及，考虑人类、自主系统或两者可能因多种因素（如感知）而出现故障的场景变得尤为重要。人类或自主智能体的故障可能导致性能水平下降，甚至引发严重事故（如伤害或死亡）。本文聚焦于人机混合协作场景，其中管理智能体需判断何时执行委派任务，并决定应由人类还是自主系统接管控制权。在此背景下，管理者将基于人类与自主智能体因感知能力及潜在缺陷导致的故障概率，评估其最优行动策略。我们建模了环境上下文如何引发或加剧感知缺陷的机制。这些上下文场景要求管理者学习将能力特征与决策适用性进行关联。由此，我们证明了强化学习（RL）管理者能够纠正上下文-委派关联偏差，并协助混合智能体团队实现优于任何独立工作智能体的表现。