  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.
