首页> 外文会议>AAAI Symposium on Formal Verification and Modeling in Human-Machine Systems >Fine Grain Modeling of Task Deviations for Assessing Qualitatively the Impact of Both System Failures and Human Error on Operator Performance
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Fine Grain Modeling of Task Deviations for Assessing Qualitatively the Impact of Both System Failures and Human Error on Operator Performance

机译:用于评估系统故障和人为错误对操作员性能的影响的优化晶粒模型

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Operators of critical interactive systems are trained and qualified before being allowed to operate critical systems in "real" contexts. However, during operation, things might happen differently from during training sessions as system failures may occur and operators may make errors when interacting with the system. Both events may also be cross-related as a misunderstanding of a system failure can lead to an erroneous subsequent operation. The proposed approach focuses on assessing the impact that potential failures and/or human errors may have on human performance. This analysis targets the design and development phases of the system, when user tasks are analyzed in order to build the right system (i.e. corresponding to the users' needs and activities they have to perform on the system). We use a task modeling notation for describing precisely operators' activities as well as information, knowledge and objects required for performing these activities. These task models are then augmented into several variants through integration of potential system failure patterns (with associated recovery tasks) and human error patterns. The produced deviated task models are used to assess the impact of the task deviation on the operators' performance.
机译:在允许在“真实”的上下文中允许在“真实”的上下文中运行关键系统之前培训并限定关键交互式系统的运营商。然而,在操作期间,由于系统故障可能发生的系统故障和操作员可以在与系统交互时,操作员可能会发生不同。这两个事件也可能是交叉相关的,因为对系统故障的误解可能导致错误的后续操作。该拟议方法侧重于评估潜在失败和/或人类错误可能对人类绩效的影响。该分析针对系统的设计和开发阶段,当分析用户任务以构建正确的系统(即,对应于它们必须在系统上执行的用户的需求和活动)。我们使用任务建模符号来描述恰好运营商的活动以及执行这些活动所需的信息,知识和对象。然后通过集成潜在的系统故障模式(具有相关恢复任务)和人为错误模式,将这些任务模型增强到多个变体中。所产生的偏移任务模型用于评估任务偏差对运营商性能的影响。

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