使用AI助手在动态自稳定任务中对抗空间定向障碍
Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants
AI总结：
本研究探讨AI助手在动态自稳定任务中对抗空间定向障碍的潜力，通过训练数字孪生和强化学习等模型提供纠正提示，发现基于强化学习的助手虽客观更有效但人类信任度和偏好度较低。
摘要：
空间定向障碍是致命飞机事故的主要原因。本文探讨了AI智能体通过提供改善空间定向障碍的提示和纠正措施来帮助飞行员保持平衡并防止不可恢复失控的潜力。研究使用多轴旋转系统（MARS）收集了人类受试者在航天模拟条件下自平衡的数据。我们基于这些数据训练模型，创建了“数字孪生”，以展示不同熟练度人类的性能特征。随后，我们训练了各种强化学习和深度学习模型，以便在预测到失控时提供纠正提示。数字孪生与助手模型随后共同执行一个编程有相同物理特性的虚拟倒立摆（VIP）任务。从这些模拟中，我们根据坠毁频率和距平衡方向的平均距离等任务指标，挑选出5个表现最佳的助手。这些助手被用于一项共同表现研究，20名新的人类受试者执行了空间信息降级版本的VIP任务。我们表明，某些AI助手能够提升人类表现，并且基于强化学习的助手在客观上更有效，但被人类评为信任度和偏好度较低。
英文摘要：
Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans.
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。
