A Framework and System for Intelligent Interruption Management

智能中断管理的框架和系统

• 批准号: 0534462
• 负责人: Brian Bailey
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-11-01 至 2009-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534462&HistoricalAwards=false
• 关键词: Framework; System; Intelligent; Interruption; Management

This research addresses one of the most important problems in human-computer interaction: interruption. E-mail notifications, agent requests, system alerts, and instant messages all periodically interrupt a user's tasks. Studies show that when tasks are interrupted at random moments, users take more time to complete tasks, commit more errors, and experience more frustration, annoyance, and anxiety than when tasks are interrupted at scheduled, opportune moments. In safety critical domains a small response delay or error committed due to poorly timed interruption can cost human lives or cause catastrophic damage, while in office settings increased frustration and anxiety hurt the user experience. When extrapolated over millions of users, the collective impact of interruption is quite remarkable. Nevertheless, users often desire or need the benefits that proactive systems provide, e.g., enabling near instant communication, maintaining awareness of information, being reminded of upcoming activities, learning to perform complex tasks, etc. The PI's basic hypothesis is that rather than interrupt immediately, proactive systems should wait for an opportune moment, that is to say a moment of lower mental workload such as at a task or subtask boundary, in a task sequence. In this research the PI will develop a theoretically-grounded framework and software that enable users to retain the benefits of proactive systems while mitigating negative effects of periodic interruption, thereby allowing users to be more productive and to experience less frustration, annoyance, and anxiety when multi-tasking. The framework will consist of empirically-based heuristics for assigning costs of interruption to various moments in a task, a description language for concisely expressing a broad range of user task behaviors, a task monitor that tracks execution of user tasks and builds a predictive model of execution, a decision algorithm that determines when to interrupt given a time window and a user's current position in a task, related tools, and empirical results and lessons. As part of the framework, the PI will develop attention manager software that defers granting requests for user attention until opportune (i.e., low cost) moments during task execution., yet which is useful even for requests with short timeframes due to urgency or relevance. The PI will empirically compare how well his attention manager balances awareness of information with mitigation of negative impact relative to other known strategies for interruption management.Broader Impacts: This research will refine and demonstrate a method for aligning mental workload to a model of task execution that will facilitate the use of workload as a new metric for evaluating complex interfaces. Project outcomes (including software) will be widely disseminated. The PI will furthermore integrate results from this research into a graduate course on human-computer interaction that offers multi-disciplinary perspectives on human attention; and will encourage participation from under-represented groups by funding at least one graduate assistant from an under-represented group for this research and by hosting two students from UIUC's Summer Research Opportunities Program each summer.

这项研究解决了人机交互中最重要的问题之一：中断。电子邮件通知、代理请求、系统警报和即时消息都会定期中断用户的任务。研究表明，当任务在随机时刻被中断时，用户完成任务所花费的时间、犯下的错误和经历的挫折、烦恼和焦虑都比在预定的时机被中断时更多。在安全关键域中，由于不恰当的时间中断而导致的微小响应延迟或错误可能会导致人员死亡或造成灾难性的破坏，而在办公室环境中，沮丧和焦虑的增加会损害用户体验。当推算超过数百万用户时，中断的集体影响是相当显著的。然而，用户通常希望或需要主动式系统提供的益处，例如，实现近乎即时的通信、保持对信息的感知、被提醒即将到来的活动、学习执行复杂任务等。PI的基本假设是，主动式系统不应立即中断，而应等待适当的时刻，即，在任务序列中，诸如在任务或子任务边界处的较低脑力劳动的时刻。在这项研究中，PI将开发一个理论上站稳脚跟的框架和软件，使用户能够保留主动式系统的优势，同时减轻周期性中断的负面影响，从而使用户在多任务处理时更高效，体验更少的挫折、烦恼和焦虑。该框架将包括：基于经验的启发式方法，用于将中断的代价分配给任务中的各个时刻；描述语言，用于简明地表达广泛的用户任务行为；任务监视器，用于跟踪用户任务的执行并建立执行的预测模型；决策算法，用于确定何时中断给定的时间窗口和用户在任务中的当前位置；相关工具；以及经验结果和经验教训。作为该框架的一部分，PI将开发注意力管理器软件，将批准用户注意力的请求推迟到任务执行过程中的适当(即，低成本)时刻，但即使对于由于紧急或相关性而具有较短时间框架的请求，这也是有用的。与其他已知的中断管理策略相比，PI将经验性地比较他的注意经理如何平衡对信息的感知与缓解负面影响。广泛影响：这项研究将改进并演示一种将精神工作负载与任务执行模型相匹配的方法，该方法将促进使用工作负载作为评估复杂界面的新度量。将广泛传播项目成果(包括软件)。PI将进一步将这项研究的成果整合到一门关于人机交互的研究生课程中，该课程提供关于人类注意力的多学科观点；并将通过资助至少一名来自代表性不足群体的研究生助理进行这项研究，以及每年夏天接待两名来自UIUC暑期研究机会项目的学生，来鼓励代表性不足群体的参与。