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Evaluating the applicability of current models of workload to peer-based human-robot teams

机译:评估当前工作负载模型对基于对等人的机器人团队的适用性

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Human-Robot peer-based teams are evolving from a far-off possibility into a reality. Human Performance Moderator Functions (HPMFs) can be used to predict human behavior by incorporating the effects of internal and external influences such as fatigue and workload. The applicability of HPMFs to human-robot teams is not proven. The presented research focuses on determining the applicability of workload HPMFs in team tasks for first response mass casualty triage incidents between a Human-Human and a Human-Robot team. A model representing workload for each team was developed using IMPRINT Pro. The results from an empirical evaluation were compared to the model results. While significant differences between the two conditions were not found in all data, there was a general trend that workload in the human-robot condition was slightly lower than the workload experienced in the human-human condition. This trend was predicted by the IMPRINT Pro models. These results are the first to indicate that existing HPMFs can be applied to human-robot peer-based teams.
机译:基于人类机器人的对等团队正在从遥远的可能性演变为现实。人类绩效主持人功能(HPMF)可通过结合内部和外部影响(例如疲劳和工作量)的影响来预测人类行为。 HPMF对人类机器人团队的适用性尚未得到证明。提出的研究重点在于确定工作量HPMF在团队任务中对人与人和机器人团队之间的第一反应大规模伤亡分类事件的适用性。使用IMPRINT Pro开发了代表每个团队工作量的模型。将经验评估的结果与模型结果进行比较。虽然并非在所有数据中都发现这两种情况之间的显着差异,但总的趋势是,人机交互条件下的工作量略低于人机交互条件下的工作量。这种趋势是由IMPRINT Pro模型预测的。这些结果是第一个表明现有HPMF可以应用于基于人类机器人对等团队的团队。

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