Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task
<|reference_start|>Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task: Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. When AI agents with ToM capability collaborate with humans, Mutual Theory of Mind (MToM) arises in such human-AI teams (HATs). The MToM process, which involves interactive communication and ToM-based strategy adjustment, affects the team's performance and collaboration process. To explore the MToM process, we conducted a mixed-design experiment using a large language model-driven AI agent with ToM and communication modules in a real-time shared-workspace task. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent and the feeling of being understood. Most participants in our study believe verbal communication increases human burden, and the results show that bidirectional communication leads to lower HAT performance. We discuss the results' implications for designing AI agents that collaborate with humans in real-time shared workspace tasks.<|reference_end|>
@article{zhang2024mutual,
    title={Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with
  LLM-driven AI Agents in a Real-time Shared Workspace Task},
    author={Shao Zhang, Xihuai Wang, Wenhao Zhang, Yongshan Chen, Landi Gao, Dakuo
  Wang, Weinan Zhang, Xinbing Wang, Ying Wen},
    journal={arXiv preprint arXiv:2409.08811},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08811},
    primaryClass={cs.HC cs.AI cs.MA}
}