Queueing Network-Model Human Processor (QN-MHP): A Computational Model and a Simulation Technology for Analyzing Human Multitask Performance in Human-Computer Systems

排队网络模型人类处理器（QN-MHP）：用于分析人机系统中人类多任务性能的计算模型和仿真技术

• 批准号: 0308000
• 负责人: Yili Liu
• 金额: $ 30万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0308000&HistoricalAwards=false
• 关键词: Queueing; Network; Model; Human; Processor

Comprehensive and computational models of human performance have both scientific and practical importance to human-computer system design. This project will make a significan contribution in the area of HCI modeling. The PI will expand a computational model and a simulation technology (implementation) called Queueing Network-Model Human Processor (QN-MHP) he has been developing. QN-MHP is unique, in that it integrates two complementary approaches to cognitive modeling: the procedure and production systems approach (exemplified by the MHP/GOMS family of models, ACT-R, CAPS, EPIC, and SOAR), and the queueing network approach. Procedure and production systems models have achieved great success in modeling, and in generating the detailed procedures and actions that a person might take in performing, a wide range of tasks; their shortcoming is that although they employ mathematics to analyze specific aspects of their models, they lack mathematical theories to represent the overall structure of their models. The queueing network model is able to integrate a large number of influential mathematical models of mental structure as special cases (such as Sternberg's serial stages model, McClelland's cascade model, and Schweikert's critical path network model), and is in general well suited for modeling dynamic and complex tasks and architectural arrangements of processes; as a mathematical theory alone, however, queueing networks cannot be used to generate detailed actions of a person in specific task situations, lacking procedural knowledge a person may employ in accomplishing his/her specific goals. QN-MHP integrates these two approaches by expanding the three discrete serial stages of the MHP into three continuous-transmission subnetworks of a queueing network, by defining each server with procedure functions, and by using a GOMS-style method for task analysis. In this project, the PI will use extensive driving simulator data as a testbed to (1) study different methods of modeling concurrent tasks with QN-MHP; (2) apply queueing network theory such as optimal network load balancing and server scheduling in multitask modeling; (3) perform time-series modeling and visualization of the relationship between queueing network indices such as network sojourn time and server congestion with human performance data such as time, error, and mental workload; (4) further develop QN-MHP to cover a broader range of human performance such as providing certain servers with production capabilities for problem solving; and (5) further develop the simulation technology of QN-MHP.Broader Impacts: The QN-MHP simulator is implemented in ProModel, a widely used software package that requires minimal learning time and allows an analyst to visualize in real time the QN-MHP internal network processes, in addition to the final statistical outcomes. These features are valuable not only for interface analysis, but also for promoting teaching and training in cognitive analysis and modeling. This software and other research results will be made readily available by the PI to practitioners, researchers, and educators. With the proliferation of life-critical multimodal, multitask HCI interfaces such as in-vehicle and aviation devices, QN-MHP will significantly impact system safety and product usability.

人的行为的综合计算模型对人机系统设计具有重要的科学意义和实用价值。 该项目将在HCI建模领域做出重大贡献。 PI将扩展他一直在开发的计算模型和仿真技术（实现），称为嵌入式网络模型人类处理器（QN-MHP）。 QN-MHP是独一无二的，因为它集成了两种互补的认知建模方法：程序和产生式系统方法（例如MHP/GOMS系列模型，ACT-R，CAPS，EPIC和SOAR），以及嵌入式网络方法。 过程和产生式系统模型在建模方面取得了巨大的成功，并在生成一个人在执行各种任务时可能采取的详细过程和行动方面取得了巨大的成功;它们的缺点是，尽管它们采用数学来分析模型的特定方面，但它们缺乏数学理论来表示模型的整体结构。 认知网络模型能够整合大量有影响力的心理结构数学模型作为特例（如斯滕贝格的串行阶段模型、麦克莱兰的级联模型和施韦克特的关键路径网络模型），并且通常非常适合于对动态和复杂任务以及过程的架构安排进行建模;然而，作为单独的数学理论，认知网络不能用于生成人在特定任务情况下的详细动作，缺乏人在实现他/她的特定目标时可以采用的程序知识。 QN-MHP通过将MHP的三个离散串行阶段扩展为一个路由网络的三个连续传输子网，通过定义每个服务器的过程功能，并通过使用GOMS风格的任务分析方法，将这两种方法集成在一起。 在这个项目中，PI将使用大量的驾驶模拟器数据作为测试平台，（1）研究使用QN-MHP建模并发任务的不同方法：（2）将最优网络负载平衡和服务器调度等嵌入式网络理论应用于多任务建模;（3）执行时间-网络逗留时间、服务器拥塞等网络指标与人的关系的系列建模和可视化性能数据，如时间、错误和脑力工作量;（4）进一步开发QN-MHP，以涵盖更广泛的人类性能，如为某些服务器提供解决问题的生产能力;以及（5）进一步开发QN-MHP的仿真技术。QN-MHP仿真器在ProModel中实现，一种广泛使用的软件包，它需要最少的学习时间，并允许分析人员在真实的时间内可视化QN-MHP内部网络处理，以及最终的统计结果。 这些特征不仅对界面分析有价值，而且对促进认知分析和建模的教学和培训也有价值。 该软件和其他研究成果将由PI提供给从业者，研究人员和教育工作者。 随着生命攸关的多模式、多任务HCI接口（如车载和航空设备）的普及，QN-MHP将对系统安全性和产品可用性产生重大影响。