Automated Task-Time Interventions to Improve Teamwork using Imitation Learning
This addresses coordination challenges in domains like healthcare and disaster response, but it is incremental as it builds on existing imitation learning methods.
The paper tackles the problem of improving teamwork in time-critical domains by presenting TIC, an automated intervention approach that uses imitation learning to generate execution-time interventions, and demonstrates in synthetic multi-agent scenarios that these interventions can successfully improve team performance.
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.