Human Reliability and Interaction with Intelligent systems

对社会的安全、发展和福利至关重要的基础服务被定义为关键基础设施。这些通常是相当大的年龄，并没有设计，以科普现代的使用。此外，还存在新的非常风险，如气候变化和恐怖袭击，这些风险无法根据历史数据预测。这是由资产的相互关联性增加混淆，一个功能依赖于其他不断输入，这提供了一个严重损害一个资产性能的事件级联到其他资产的机制。虽然人类干预仍然是减轻极端事件后果的最终资源，但人类仍然是这些关键基础设施中最薄弱的环节。特别是，人类与自主和智能系统的相互作用在很大程度上仍然未知。该项目将开发模型，以评估相互关联的重要基础设施发生破坏性事件后人的表现。现有的人因可靠性分析方法集中在维持或激活系统的保护和缓解措施所需的人因行为上。这意味着他们专注于人类犯错误引发事故事件的概率。但是，如果现有的保护措施无法遏制破坏性事件的发展，则需要采取新的人类行动。现有的关于人在这一阶段的表现的研究通常包括人的逃生行为，以确定更好的逃生路线，但没有采取必要的行动，以恢复系统的人的表现。目前定性可靠性方法不提供人因错误概率，而仅提供其识别和防止或减轻人因错误的可能解决方案[2]。虽然一些安全监管机构确实接受对人误的定性分析，但概率安全评估要求人误概率。定量人因可靠性方法（如THERP、SPAR-H、HEART、CREAM和ATHEANA）经常受到不精确性的影响，导致低估或高估概率。这种不确定性可能是阻止行业采用考虑人为错误的风险评估的原因之一。本研究提出了一种创新的方法来构建和校准模型，用于使用从现有重大事故调查报告中提取的数据计算人因错误概率[1]。这种方法有可能提供描述模拟器、未遂事件和专家启发数据未完全实现的背景和场景的数据[3，4]。由于此类报告的数量通常很少，因此将通过访问模拟数据（例如，来自模拟器和从相似性中学习）获得更多信息。该方法允许最大限度地减少专家在人为错误概率定义中的判断，评估不同情景下人为错误的不确定性和可变性。

Foundational services that are essential to the security, development, and welfare of a society are defined as Critical infrastructures. These are often of considerable age and were not designed to cope with modern-day usage. In addition, there exist new extraordinary risks, such as climate change and terrorist attacks that are not predictable on the basis of historical data. This is confounded by the increased interconnectivity of assets, with the function of one dependent on constant input from others, this provides mechanisms for one event acutely damaging one assets performance to cascade to others. Although the human intervention remains the ultimate resource to mitigate the consequences of extreme events, Humans also remain the weakest link of such critical infrastructure. In particular, the interaction of human an autonomous and intelligent systems remains largely unknown. This project will develop models to assess human performance after the occurrence of a disruptive event of interconnected critical infrastructures. The existing Human Reliability Analysis methods are focused on the human actions needed to maintain or activate the protection and mitigation measures of a system. It means that they are focused on the probability of a human making an error that initiates an accident event. However, if the protection measures in place fail to contain the evolution of the disruptive event, new human actions are needed. Existing research on human performance on this phase usually covers the human escaping behaviour, to define better escape routes, but not the human performance for taking the necessary actions to recover the system. Currently qualitative reliability methods do not provide the human error probability, but only its identification and possible solutions to prevent or mitigate human errors [2]. Although some safety regulators do accept qualitative analysis on human errors, human error probabilities are required by probabilistic safety assessment. Quantitative human reliability methods such as THERP, SPAR-H, HEART, CREAM and ATHEANA are often affected by imprecision, leading to under-estimated or over-estimated probabilities. This uncertainty may be one of the causes that is preventing industries from adopting risk assessments that account for human errors. The present research proposes to develop an innovative approach to construct and calibrate a model for calculating the human error probability using data extracted from existing major accident investigation reports [1]. This approach has the potential to provide data that depict contexts and scenarios not fully achieved by simulator, near-misses and expert elicitation data [3,4]. Since the number of such reports are usually very small, additional information will be gained by accessing simulated data (e.g. from simulators and learning from similarities). The methodology allows to minimise the expert judgement in the definition of human error probability, assess the uncertainty and variability of human errors under different scenarios.