Computational Cognitive Models in Adaptive Automation: Supporting Operator Learning and Skill Retention

自动化辅助工具在工作场所和日常生活中正变得越来越普遍。例如，自动驾驶系统可以驾驶飞机；自动驾驶汽车预计将在10年内投放市场。自动化使用的一个严重的安全问题是，人工操作员可能会变得不那么积极参与，并逐渐失去技能熟练程度。在导致机上228人死亡的法航447航班事故中，由于广泛使用自动驾驶仪，一个主要原因是“在高空没有进行任何手动飞机操纵培训”(AF447最终报告，2012年)。为了提高人类自动化系统的安全性和可靠性，我将开发自适应自动化，它使用计算认知模型来预测操作员的技能保留率和脑力劳动。由此产生的适应性自动化将能够以一种“智能”的方式调整自动化辅助的水平，为操作员提供只有在他们的工作量不太高的情况下才有机会练习他们的技能。

Automation aids are becoming increasingly prevalent in the workplace and everyday life. For example, autopilot systems can fly planes; autonomous cars are expected to hit the market within 10 years. A profound safety concern of automation use is that human operators can become less actively involved and gradually lose skill proficiency. In the Air France Flight 447 accident that killed 228 people onboard, a major causing factor is “the absence of any training, at high altitude, in manual aeroplane handling”, due to extensive use of autopilot (AF 447 Final Report, 2012). To improve the safety and reliability of human-automation systems, I will develop adaptive automation that uses computational cognitive models to predict operators’ skill retention rates and mental workload. The resulting adaptive automation will be able to adjust the level of automated aid in a “smart” way, providing operators with opportunities to practice their skills only when their workloads are not too high.