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

自适应自动化中的计算认知模型：支持操作员学习和技能保留

• 批准号: RGPIN-2015-04134
• 负责人: Cao, Shi
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=589611
• 关键词: Computational; Cognitive; Models; Adaptive; Automation

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.

自动化辅助工具在工作场所和日常生活中越来越普遍。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年内上市。自动化使用的一个深刻的安全问题是，人类操作员可能会变得不那么积极参与，并逐渐失去技能熟练度。在导致228人死亡的法航447航班事故中，一个主要的原因是由于大量使用自动驾驶仪，“缺乏任何高空手动飞机操作的培训”（AF 447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，使用计算认知模型来预测操作员的技能保留率和心理工作量。由此产生的自适应自动化将能够以“智能”的方式调整自动化辅助的水平，仅在工作量不太高时为操作员提供练习技能的机会。