次优情境下的混合主动人机协同：在线贝叶斯自适应方法
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
AI总结：
本研究针对次优人机协同场景，提出在线贝叶斯自适应方法，可推断人类配合机器人协助的意愿，实验表明该方法能显著提升团队客观性能与用户主观指标。
摘要：
为实现有效的人机协同，机器人及其他人工智能（AI）智能体需推断人类伙伴的能力与行为响应模式并据此调整。多数现有研究做出了不切实际的假设，即一名或多名队友的行动接近最优。而在现实协作中，人类与自主智能体可能表现为次优，尤其是当各方仅拥有部分领域知识时。本研究开发了用于提升次优人机团队性能的计算建模与优化技术，其中人类与智能体能力不对称，且因环境知识不完整而表现为次优。我们采用在线贝叶斯方法，使机器人能在序贯决策博弈中推断人们配合其协助的意愿。用户研究显示，用户偏好与团队性能确实随机器人干预风格变化，且我们的混合主动协作方法可提升客观团队性能（p<.001），以及用户信任（p<.001）、配合机器人的意愿（p<.001）等主观指标。
英文摘要：
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 willingness to comply with the robot ($p<.001$).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
