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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716081
• 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发展速度超过了工人们的适应能力。系统能力和人类适应性之间的差距导致无法实现强大技术的生产力、安全性和安全性优势。人的因素工程可以通过确定人们相对于技术的能力和限制的能力和限制来帮助缩小这一差距。我的建议的目标是通过人的因素设计和评估方法，建立关于人们如何使用这些先进技术的知识。这些知识可以转移给希望人们最大限度地利用他们的智能系统的技术开发人员。 该计划由三条线索组成，每条线索都试图弥合人与技术之间的差距。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界面。这样的界面可以诱使人们走出注意力隧道，并帮助他们应对信息过载。在第二部分中，我们将研究专业知识和任务复杂性等因素如何缓解日益自动化的系统对人类表现的负面影响。第三，我们将开发新的图形用户界面，帮助人们理解机器决策背后的原理。有了这样的理解，人们可以就何时使用自动化以及何时在所有三个方面依靠自己的能力做出更合适的决定，我们将使用高保真模拟器，与专家参与者合作，研究与技术的真实世界互动，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、工程师和管理人员都很重要；他们都面临着在工业、国防和安全环境中部署快速发展的技术的挑战。这些知识型员工提取这些技术所蕴含的价值，以实现更高的工作效率、更高的安全性、更低的成本和环境影响。 该研究计划将培训六名研究生，让他们掌握对加拿大工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，测量人类性能，分析数据，并提取设计原则。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，以促进研究计划的发展。