In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.

翻译：在人机混合团队中，人类与人工智能或自主代理协同工作时，准确识别何时授权这些团队成员执行任务至关重要。基于人类与自主系统在任务中既可能成功也可能失败的既往案例，我们致力于训练一个委派管理代理，使其能够针对这些潜在的性能缺陷做出委派决策。此外，我们无法始终期望不同代理在相同的环境模型下运行。实际中可能出现各代理的动作与状态转换存在差异的情况。因此，我们提出的框架构建了一个管理模型，该模型通过观察团队的整体表现进行学习，且无需限制代理必须具有匹配的动力学特性。实验结果表明，在多个代理以不同环境表征方式运行的团队中，我们的管理模型能够有效执行委派决策，其性能显著优于其他团队管理方法。