利纳克相干光源仪器操作的多尺度认知交互模型

Jonathan Segal, Wan-Lin Hu, Paul Fuoss, Frank E. Ritter, Jeff Shrager
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引用次数: 0

摘要

我们描述了一个新颖的多代理、多尺度计算认知交互模型,该模型适用于里纳相干光源(LCLS)的仪器操作。作为领先的科学用户设施,LCLS 是世界上第一台硬 x 射线自由电子激光器,由美国能源部 SLAC 国家加速器实验室运营。作为世界上第一台 X 射线自由电子激光器,LCLS 的需求量很大,超额认购率很高。我们的整个项目采用认知工程学方法,通过改进实验界面和工作流程、简化任务、减少错误以及提高操作员的安全性和压力水平,来提高实验效率和科学生产力。我们的模型模拟了从几秒到几小时不等的多个认知和时间尺度上的人类认知,并在扮演多种角色(包括仪器操作员、实时数据分析师和实验管理者)的代理之间进行了模拟。该模型可以预测对操作界面和工作流程的拟议更改所产生的影响。由于模型代码是开源的,而且补充视频详细介绍了模型和结果的各个方面,因此这种方法可以应用于其他实验仪器和过程。示例结果证明了该模型在指导修改以提高操作效率和科研产出方面的潜力。我们讨论了我们的发现对复杂实验环境中认知工程的影响,并概述了未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Scale Cognitive Interaction Model of Instrument Operations at the Linac Coherent Light Source
We describe a novel multi-agent, multi-scale computational cognitive interaction model of instrument operations at the Linac Coherent Light Source (LCLS). A leading scientific user facility, LCLS is the world's first hard x-ray free electron laser, operated by the SLAC National Accelerator Laboratory for the U.S. Department of Energy. As the world's first x-ray free electron laser, LCLS is in high demand and heavily oversubscribed. Our overall project employs cognitive engineering methodologies to improve experimental efficiency and scientific productivity by refining experimental interfaces and workflows, simplifying tasks, reducing errors, and improving operator safety and stress levels. Our model simulates aspects of human cognition at multiple cognitive and temporal scales, ranging from seconds to hours, and among agents playing multiple roles, including instrument operator, real time data analyst, and experiment manager. The model can predict impacts stemming from proposed changes to operational interfaces and workflows. Because the model code is open source, and supplemental videos go into detail on all aspects of the model and results, this approach could be applied to other experimental apparatus and processes. Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output. We discuss the implications of our findings for cognitive engineering in complex experimental settings and outline future directions for research.
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