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：有效的人-人及人-自主团队协作至关重要，但往往难以臻于完美。这一挑战在医疗和灾害响应等时间紧迫领域尤为突出，时间压力使得协调愈加困难，协调不当的后果可能极其严重。为改善这些及其他领域的团队协作，我们提出TIC：一种旨在提升团队成员间协调性的自动化干预方法。该方法利用多智能体模仿学习算法BTIL，首先从历史任务执行数据中学习团队行为的生成模型，随后借助该生成模型及团队任务目标（共享奖励），通过算法生成执行时机的干预策略。我们在智能体需在环境不完全可观测条件下做出分散决策的合成多智能体团队场景中评估了该方法。实验表明，自动化干预能有效提升团队绩效，并为设计改善团队协作的自主智能体提供了新思路。