For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly. Most prior works make the unrealistic assumption that one or more teammates can act near-optimally. In real-world collaboration, humans and autonomous agents can be suboptimal, especially when each only has partial domain knowledge. In this work, we develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams, where the human and the agent have asymmetric capabilities and act suboptimally due to incomplete environmental knowledge. We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game. Our user studies show that user preferences and team performance indeed vary with robot intervention styles, and our approach for mixed-initiative collaborations enhances objective team performance ($p<.001$) and subjective measures, such as user's trust ($p<.001$) and perceived likeability of the robot ($p<.001$).

翻译：为实现高效的人机团队协作，机器人及其他人工智能（AI）智能体必须推断人类伙伴的能力与行为响应模式，并据此进行自适应调整。以往研究大多假设一个或多个团队成员能够以近乎最优方式行动，这一假设在现实中难以成立。真实协作场景中，人类与自主智能体均可能呈现非最优状态，尤其在双方仅具备部分领域知识时。本研究开发了计算建模与优化技术，旨在提升非最优条件下人机团队的性能表现，其中人类与智能体因环境知识不完整而表现出非对称能力与非最优行为。我们采用在线贝叶斯方法，使机器人在序贯决策博弈中推断人类对其协助的遵从意愿。用户研究表明，用户偏好与团队绩效确实随机器人干预风格变化而改变，且我们提出的混合主动协作方法能显著提升客观团队绩效（p<.001）及主观指标，如用户信任度（p<.001）与机器人感知喜爱度（p<.001）。