  Reaching a consensus on the team plans is vital to human-AI coordination.
Although previous studies provide approaches through communications in various
ways, it could still be hard to coordinate when the AI has no explainable plan
to communicate. To cover this gap, we suggest incorporating external models to
assist humans in understanding the intentions of AI agents. In this paper, we
propose a two-stage paradigm that first trains a Theory of Mind (ToM) model
from collected offline trajectories of the target agent, and utilizes the model
in the process of human-AI collaboration by real-timely displaying the future
action predictions of the target agent. Such a paradigm leaves the AI agent as
a black box and thus is available for improving any agents. To test our
paradigm, we further implement a transformer-based predictor as the ToM model
and develop an extended online human-AI collaboration platform for experiments.
The comprehensive experimental results verify that human-AI teams can achieve
better performance with the help of our model. A user assessment attached to
the experiment further demonstrates that our paradigm can significantly enhance
the situational awareness of humans. Our study presents the potential to
augment the ability of humans via external assistance in human-AI
collaboration, which may further inspire future research.
