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:有效的人类和人类自主团队合作至关重要,但往往对完善工作提出挑战。挑战在时间紧迫的领域特别相关,如医疗保健和灾害应对,因为时间压力使协调越来越难以实现,协调不完善的后果可能十分严重。为了改进这些和其他领域的团队合作,我们介绍TIC:改进团队成员之间协调的自动干预方法。使用多试剂模拟学习算法,我们的方法首先从以往的任务执行数据中学习团队行为的基因模型。接着,我们利用学习的基因模型和团队任务目标(共享奖励),从逻辑上生成执行时间干预措施。我们评估我们在合成多试剂团队情景中的做法,即团队成员在不完全注意环境的情况下作出分散决策。实验表明,自动化干预措施能够成功改善团队业绩,并展示自主代理人员改进团队合作的设计。</s>