Computing the Effects of Operator Attention Allocation in Human Control of Multiple Robots
Computing the Effects of Operator Attention Allocation in Human Control of Multiple Robots
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DOI：
10.1109/tsmca.2010.2084082
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
J. Crandall;M. Cummings;Mauro Della Penna;P. M. Jong
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文献类型：
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作者：
J. Crandall;M. Cummings;Mauro Della Penna;P. M. Jong
在人工操作员管理多个半自动任务的时间关键型系统中，操作员未能及时将注意力集中在高优先级任务上可能会降低系统的有效性，并可能导致灾难性的后果。这些系统必须整合基于计算机的技术，帮助操作员在正确的时间将注意力放在正确的任务上，才能成功。在这一过程中帮助操作员的一种方法是计算操作员的注意力应该集中在哪里，然后使用该计算来影响操作员的行为。在本文中，我们分析了一种特殊的建模方法进行这种计算的能力，以便在人-多机器人系统中进行有效的注意力分配。我们的结果表明，简单地计算和规定操作者应该如何分配他们的注意力是不够的。相反，在随机领域，内生或外生环境的微小变化都会显著影响模型的保真度，模型预测应该引导而不是支配操作者的注意力资源，以便操作者能够有效地行使他们的判断和经验。
In time-critical systems in which a human operator supervises multiple semiautomated tasks, failure of the operator to focus attention on high-priority tasks in a timely manner can lower the effectiveness of the system and potentially result in catastrophic consequences. These systems must integrate computer-based technologies that help the human operator place attention on the right tasks at the right times to be successful. One way to assist the operator in this process is to compute where the operator's attention should be focused and then use this computation to influence the operator's behavior. In this paper, we analyze the ability of a particular modeling method to make such computations for effective attention allocation in human-multiple-robot systems. Our results demonstrate that it is not sufficient to simply compute and dictate how operators should allocate their attention. Rather, in stochastic domains, where small changes in either the endogenous or exogenous environment can dramatically affect model fidelity, model predictions should guide rather than dictate operator attentional resources so that operators can effectively exercise their judgment and experience.