Effective human-human and human-autonomy teamwork is critical but often challenging to perfect. The challenge is particularly relevant in time-critical domains, such as healthcare and disaster response, where the time pressures can make coordination increasingly difficult to achieve and the consequences of imperfect coordination can be severe. To improve teamwork in these and other domains, we present TIC: an automated intervention approach for improving coordination between team members. Using BTIL, a multi-agent imitation learning algorithm, our approach first learns a generative model of team behavior from past task execution data. Next, it utilizes the learned generative model and team's task objective (shared reward) to algorithmically generate execution-time interventions. We evaluate our approach in synthetic multi-agent teaming scenarios, where team members make decentralized decisions without full observability of the environment. The experiments demonstrate that the automated interventions can successfully improve team performance and shed light on the design of autonomous agents for improving teamwork.

翻译：高效的人-人协作与人-自主系统协作至关重要，但往往难以臻于完善。这一挑战在时间紧迫的领域尤为突出，例如医疗救护与灾害应急响应，时间压力使得协调愈发困难，而协调不力的后果可能极为严重。为提升这些及其他领域的团队协作，我们提出TIC：一种旨在改善团队成员间协调性的自动化干预方法。该方法采用多智能体模仿学习算法BTIL，首先基于历史任务执行数据学习团队行为的生成式模型；随后，利用习得的生成式模型与团队任务目标（共享奖励）在任务执行过程中自动生成干预策略。我们在合成多智能体协作场景中评估了该方法——该场景下团队成员需在环境不完全可观测条件下进行去中心化决策。实验表明，自动化干预能够有效提升团队绩效，并为设计改善团队协作的自主智能体提供了重要启示。