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：随着具备自主行动能力或增强人类活动的智能系统日益普及，考虑人类、自主系统或两者因感知等多重因素导致故障的情景至关重要。人类或自主代理的故障可能导致性能下降，甚至引发伤害或死亡等严重后果。本研究聚焦人机混合协作场景，其中管理代理负责识别何时进行委派分配，并决定应由人类还是自主系统获得控制权。在此背景下，管理者将根据人类与自主代理基于感知能力及潜在缺陷的故障概率评估最优策略。我们构建了环境因素如何诱发或加剧感知缺陷的模型，这些情景要求管理者学习将能力属性与决策适切性相关联。由此，我们证明了强化学习管理代理能够修正情景-委派关联机制，并辅助混合代理团队在性能上超越任何独立运作的代理。