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.

翻译：达成团队计划共识对于人机协作至关重要。尽管先前研究通过多种方式的通信提供了方法，但当AI缺乏可解释的计划进行沟通时，协同仍可能存在困难。为弥补这一不足，我们建议引入外部模型来辅助人类理解AI智能体的意图。本文提出两阶段范式：首先从目标智能体收集的离线轨迹中训练心理理论模型，然后通过实时显示目标智能体的未来动作预测，将其应用于人机协作过程。该范式将AI智能体视为黑箱，因此可适用于任何智能体的改进。为验证该范式，我们进一步实现了基于Transformer的预测器作为心理理论模型，并开发了扩展的在线人机协作实验平台。综合实验结果表明，借助我们的模型，人机团队可获得更优表现。实验附带的用户评估进一步证明，该范式能显著提升人类的情境感知能力。本研究展现了通过外部辅助增强人机协作中人类能力的潜力，有望为未来研究提供启发。