Heard Jamison, Baskaran Prakash, Adams Julie A
Adaptive Human-Robot Teaming Lab, Electrical and Microelectornic Engineering Department, Rochester Institute of Technology, Rochester, NY, United States.
Human-Machine Teaming Lab, Robotics Department, Oregon State University, Corvallis, OR, United States.
Front Neurorobot. 2022 Sep 13;16:973967. doi: 10.3389/fnbot.2022.973967. eCollection 2022.
Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used to manage the human's workload state intelligently, as the overall workload is related to task performance. Intelligent human-machine teaming systems rely on a facet of the human's state to determine how interaction occurs, but typically only consider the human's state at the current time step. Future task performance predictions may be leveraged to determine if adaptations need to occur in order to prevent future performance degradation. An individualized task performance prediction algorithm that relies on a multi-faceted human workload estimate is shown to predict a supervisor's task performance accurately. The analysis varies the prediction time frame (from 0 to 300 s) and compares results to a generalized algorithm.
人机团队被部署在各种任务环境和范式中，这些环境和范式可能具有高昂的故障成本（例如核电站）。至关重要的是，机器团队成员能够与人类有效互动，同时不降低任务绩效。由于总体工作量与任务绩效相关，这些互动可用于智能管理人类的工作量状态。智能人机协作系统依赖于人类状态的一个方面来确定互动如何发生，但通常只考虑当前时间步长的人类状态。未来的任务绩效预测可用于确定是否需要进行调整，以防止未来绩效下降。一种依赖多方面人类工作量估计的个性化任务绩效预测算法被证明能够准确预测主管的任务绩效。该分析改变了预测时间范围（从0到300秒），并将结果与一种通用算法进行比较。
