HCC: Medium: A Multi-disciplinary Computational Model of Worker, Task, and Context for Real-time Adaptive Procedural Systems (R-TAPS)

HCC：中：实时自适应程序系统 (R-TAPS) 的工作人员、任务和上下文的多学科计算模型

• 批准号: 2106963
• 负责人: Farzan Sasangohar
• 金额: $ 80万
• 依托单位: The Texas A&M University System HSC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106963&HistoricalAwards=false
• 关键词: HCC; Medium; Multi; disciplinary; Computational

This project will advance fundamental knowledge of how to consider psychological, contextual, and technical issues in designing tools to help workers in safety-critical systems follow work procedures and reduce human errors. One of the most frequent causes of incidents in high-risk facilities is when workers deviate from procedures. Even well-designed procedures and tools for following them are not immune, because of a number of factors including workers’ experience with the task, how often they perform it, and how well the procedure design fits their cognitive style. Together, these findings indicate that rather than single, fixed procedure formats, better safety is likely to come when procedures adapt to the person and the context they are used by. To do this, the project team will bring together expertise from psychology, human factors, and engineering to develop models of the important factors that affect people’s performance around procedures, and to design procedures based on sets of behavioral “scripts” that can be activated in appropriate situations and tuned to particular workers. The work will be done in the context of the oil and gas industry, a domain that has both high societal value and high safety risks, with the goal of improving worker safety in real deployments in this domain along with general methods that can be applied to studying and designing safety procedures in other safety-critical domains. The team will also provide career development opportunities for several students, with a focus on increasing participation of underrepresented groups in research.To do this work, the project team will leverage cognitive, linguistic, and systems methods to identify worker, task, and context attributes relevant to procedure performance. This project's specific research objective is to map these factors onto a computationally tractable framework, the Multi-disciplinary Interactive Behavior Triad, that will form the basis of a script-based Real-Time Adaptive Procedure System (R-TAPS) that uses the model and data collected from work context, matching a worker’s current context to known factors and design features and automatically generating personalized procedural guides. Data for both building and evaluating the models and the R-TAPS system will be collected from workers recruited at Shell's Robert Training Facility (RTF) with a high-fidelity simulation of offshore operations to test and evaluate the R-TAPS tool. More generally, the research contributes to human-machine interaction by creating a framework that allows for methods from multiple disciplines associated with human behavior to be integrated and applied to an AI methodology, where all the relevant constructs can be applied and translated to the design of effective technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将推进如何在设计工具时考虑心理，环境和技术问题的基本知识，以帮助安全关键系统中的工人遵循工作程序并减少人为错误。在高风险设施中，事故最常见的原因之一是工人偏离程序。即使是精心设计的程序和工具，也不能幸免，因为有很多因素，包括工人的任务经验，他们执行任务的频率，以及程序设计是否适合他们的认知风格。总之，这些研究结果表明，而不是单一的，固定的程序格式，更好的安全性可能会来时，程序适应的人和他们所使用的环境。为此，项目团队将汇集心理学、人为因素和工程学方面的专业知识，开发影响人们在程序方面表现的重要因素的模型，并根据可以在适当情况下激活并针对特定工人调整的行为“脚本”集设计程序。这项工作将在石油和天然气行业的背景下进行，这是一个具有高社会价值和高安全风险的领域，其目标是在该领域的真实的部署中提高工人的安全性，同时采用沿着可应用于研究和设计其他安全关键领域安全程序的一般方法。该团队还将为几名学生提供职业发展机会，重点是增加研究中代表性不足的群体的参与。为了完成这项工作，项目团队将利用认知，语言和系统方法来识别与程序性能相关的工人，任务和上下文属性。该项目的具体研究目标是将这些因素映射到一个计算易处理的框架上，即多学科交互行为三元组，该框架将构成基于脚本的实时自适应程序系统（R-TAPS）的基础，该系统使用从工作环境中收集的模型和数据，将工人的当前环境与已知因素和设计特征相匹配，并自动生成个性化的程序指南。用于建立和评估模型以及R-TAPS系统的数据将从壳牌罗伯特培训设施（RTF）招募的工人那里收集，并对海上作业进行高保真模拟，以测试和评估R-TAPS工具。更一般地说，这项研究通过创建一个框架来促进人机交互，该框架允许将与人类行为相关的多个学科的方法集成并应用于人工智能方法，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。