Human Interaction with Multi-level, Transparent and Adaptive Automation

与多层次、透明和自适应自动化的人机交互

• 批准号: RGPIN-2019-06773
• 负责人: Jamieson, Greg
• 金额: $ 2.62万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759658
• 关键词: Human; Interaction; Multi; level; Transparent

The capabilities of machine intelligence and automation technologies are advancing faster than workers can adapt to them. The gap between system capabilities and human adaptability results in significant failures to realize the productivity, safety, and security benefits of powerful technologies. Human Factors Engineering can help to close that gap by identifying the capabilities and limitations of people relative to the capabilities and limitations of technology. The objective of my proposal is to build knowledge about how people use these advanced technologies through human factors design and evaluation methods. This knowledge can be transferred to technology developers who want people to get the most out of their intelligent systems. The program consists of three threads, each of which seeks to close the gap between people and technology. In the first, we will develop graphical interfaces that use machine learning techniques to adapt to the cognitive states of users. Such interfaces can coax people out of attentional tunnels and help them to cope with information overload. In the second, we will study how factors like expertise and task complexity might alleviate the negative impacts of increasingly automated systems on human performance. In the third, we will develop new graphical user interfaces to help people understand the rationales behind machine decisions. Given such understanding, people can make more appropriate decisions about when to use automation and when to rely on their own capabilities In all three threads, we will use high-fidelity simulators, work with expert participants, study real-world interaction with technology, and follow accepted ethical and inclusive research practices. This research is important to technology developers, workers, soldiers, engineers and managers; all of whom face the challenge of deploying rapidly advancing technology in industrial, defence, and security contexts. These knowledge workers extract the value embedded in these technologies to achieve greater productivity, increased safety, and reduced costs and environmental impacts. The research program will train six graduate students, equipping them with skills that are essential to Canadian industry competitiveness and national security. They will learn how to apply machine learning techniques, design graphical user interfaces, conduct simulator-based experiments, measure human performance, analyze data, and extract design principles. The program will also create opportunities to establish new partnerships with Canadian companies to further the research program.

机器智能和自动化技术的能力比工人适应的能力更快。系统能力和人类适应性之间的差距导致实现强大技术的生产力，安全性和安全益处的重大失败。人力因素工程可以通过识别人们相对于技术的能力和局限性的能力和局限性来帮助缩小差距。我的建议的目的是建立有关人们如何通过人为因素设计和评估方法使用这些先进技术的知识。这些知识可以转移给希望人们从智能系统中获得最大收益的技术开发人员。该程序由三个线程组成，每个线程都试图缩小人员与技术之间的差距。首先，我们将开发使用机器学习技术来适应用户认知状态的图形接口。这样的界面可以哄骗人们从注意力的隧道中伸出来，并帮助他们应对信息过载。在第二个中，我们将研究专业知识和任务复杂性等因素如何减轻日益自动化系统对人类绩效的负面影响。在第三个中，我们将开发新的图形用户界面，以帮助人们了解机器决策背后的理由。鉴于这种理解，人们可以就何时使用自动化以及何时依靠自己在所有三个线程中依靠自己的功能做出更适当的决定，我们将使用高保真模拟器，与专家参与者合作，研究与技术的现实互动，并遵循公认的道德和包容性研究实践。 这项研究对技术开发人员，工人，士兵，工程师和经理很重要。所有人都面临着在工业，国防和安全环境中迅速发展技术的挑战。这些知识工作者提取这些技术嵌入的价值，以提高生产率，提高安全性以及成本和环境影响。该研究计划将培训六名研究生，为他们提供对加拿大行业竞争力和国家安全至关重要的技能。他们将学习如何应用机器学习技术，设计图形用户界面，进行基于模拟器的实验，衡量人类性能，分析数据并提取设计原理。该计划还将创造机会与加拿大公司建立新的合作伙伴关系以进一步进一步研究计划。