Automated Task-Time Interventions to Improve Teamwork using Imitation Learning
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning
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DOI：
10.48550/arxiv.2303.00413
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Sang-Wook Seo;Bing-zheng Han;Vaibhav Unhelkar
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文献类型：
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作者：
Sang-Wook Seo;Bing-zheng Han;Vaibhav Unhelkar
有效的人与人之间和人与人之间的自主团队合作是至关重要的，但要做到完美往往具有挑战性。这一挑战在时间紧迫的领域尤其重要，例如医疗保健和灾害应对，在这些领域，时间压力可能使协调变得越来越困难，协调不完美的后果可能会很严重。为了改善这些领域和其他领域的团队合作，我们提出了TIC：一种自动化干预方法，用于改善团队成员之间的协调。使用BTIL(一种多智能体模拟学习算法)，我们的方法首先从过去的任务执行数据中学习团队行为的生成模型。接下来，它利用学习的生成模型和团队的任务目标(共享奖励)以算法生成执行时干预。我们在合成多智能体团队场景中评估我们的方法，在这种场景中，团队成员在没有完全可观察到环境的情况下做出分散的决策。实验表明，自动干预可以成功地提高团队绩效，并为设计自主代理来提高团队合作提供了参考。
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.