On the Utility of Accounting for Human Beliefs about AI Behavior in Human-AI Collaboration
<|reference_start|>On the Utility of Accounting for Human Beliefs about AI Behavior in Human-AI Collaboration: To enable effective human-AI collaboration, merely optimizing AI performance while ignoring humans is not sufficient. Recent research has demonstrated that designing AI agents to account for human behavior leads to improved performance in human-AI collaboration. However, a limitation of most existing approaches is their assumption that human behavior is static, irrespective of AI behavior. In reality, humans may adjust their action plans based on their observations of AI behavior. In this paper, we address this limitation by enabling a collaborative AI agent to consider the beliefs of its human partner, i.e., what the human partner thinks the AI agent is doing, and design its action plan to facilitate easier collaboration with its human partner. Specifically, we developed a model of human beliefs that accounts for how humans reason about the behavior of their AI partners. Based on this belief model, we then developed an AI agent that considers both human behavior and human beliefs in devising its strategy for working with humans. Through extensive real-world human-subject experiments, we demonstrated that our belief model more accurately predicts humans' beliefs about AI behavior. Moreover, we showed that our design of AI agents that accounts for human beliefs enhances performance in human-AI collaboration.<|reference_end|>
@article{yu2024on,
    title={On the Utility of Accounting for Human Beliefs about AI Intention in
  Human-AI Collaboration},
    author={Guanghui Yu, Robert Kasumba, Chien-Ju Ho, and William Yeoh},
    journal={arXiv preprint arXiv:2406.06051},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06051},
    primaryClass={cs.AI cs.HC cs.LG}
}