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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679187
• 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.*** Focusing on the transportation domain, this research program will investigate three scenarios: indoor navigation, driving, and air traffic control. They are selected to comprehensively examine the cognitive modeling approach to adaptive automation. In each scenario, I will conduct experimental investigation, model development, and adaptive automation design and test. The results will have both practical and theoretical value.*** The resulting methods and prototypes are expected to generate intellectual property and be transferred to industry for the production of smarter and safer automation systems. *** The cognitive modeling approach will allow early design evaluation of human-automation systems in simulation environments, reducing the cost and risk of human test. *** The modeling theory and mechanisms developed in this program will advance the theories of unified cognition and human performance modeling.*** The results will also inform and benefit applications in other domains such as education, healthcare, law enforcement, and entertainment, where human performance and user experience are among the most important concerns. *** This program will train HQP (4 Master's and 1 PhD) for the future transportation sector in Canada. They will master cognitive architecture modeling, systems integration, automation reliability test, and user experiment skills trained in this multidisciplinary research program. These skills enable them to quantitatively analyze and predict human-automation system performance, and the skills will be essential for their future innovative research and design work transforming the transportation sector. The knowledge gained in this program is expected to be published in top-tier journals and taught in class to prepare the Canadian workforce in the competitive global economy.**

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。该研究计划以交通领域为重点，将研究三种场景：室内导航，驾驶和空中交通管制。他们被选中全面检查的认知建模方法，自适应自动化。在每个场景中，我将进行实验调查，模型开发，自适应自动化设计和测试。研究结果具有一定的理论价值和实用价值 *。 由此产生的方法和原型预计将产生知识产权，并转移到工业生产更智能，更安全的自动化系统。* 认知建模方法将允许在模拟环境中对人类自动化系统进行早期设计评估，降低人类测试的成本和风险。* 本计划开发的建模理论和机制将推动统一认知和人类绩效建模理论的发展。 研究结果还将为教育、医疗保健、执法和娱乐等其他领域的应用提供信息，并使其受益，在这些领域，人类表现和用户体验是最重要的问题之一。*** 该计划将为加拿大未来的运输部门培养HQP（4名硕士和1名博士）。他们将掌握认知架构建模，系统集成，自动化可靠性测试和用户实验技能在这个多学科的研究计划培训。这些技能使他们能够定量分析和预测人类自动化系统的性能，这些技能对于他们未来的创新研究和设计工作至关重要。在该计划中获得的知识预计将发表在顶级期刊上并在课堂上教授，以使加拿大劳动力在竞争激烈的全球经济中做好准备。**