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。这些通常都有相当长的历史，并不是为了适应现代的使用而设计的。此外，还存在着气候变化、恐怖袭击等新的特殊风险，这些风险在历史数据基础上是无法预测的。这与资产之间日益增强的互联性相混淆，一个资产的功能依赖于其他资产的持续输入，这提供了一种机制，使一个事件严重损害一种资产的表现，进而影响到其他资产。虽然人为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、自主系统和智能系统之间的相互作用在很大程度上仍然未知。该项目将开发模型，以评估相互关联的关键基础设施发生破坏性事件后的人类表现。现有的人类可靠性分析方法侧重于维持或激活系统的保护和缓解措施所需的人类行为。这意味着他们关注的是人为失误引发事故事件的可能性。然而，如果现有的保护措施无法遏制破坏性事件的演变，就需要采取新的人类行动。现有的关于这一阶段人类行为的研究通常涵盖人类的逃离行为，以确定更好的逃离路线，而不是采取必要行动恢复系统的人类行为。目前定性可靠性方法不能提供人为错误概率，而只能提供人为错误的识别和防止或减轻人为错误的可能解决方案。虽然一些安全监管机构确实接受对人为错误进行定性分析，但概率安全评估需要人为错误概率。定量的人类可靠性方法，如THERP、SPAR-H、HEART、CREAM和athana，经常受到不精确的影响，导致低估或高估概率。这种不确定性可能是阻止行业采用人为错误风险评估的原因之一。本研究提出了一种创新的方法，利用从现有重大事故调查报告中提取的数据来构建和校准计算人为错误概率的模型[1]。这种方法有可能提供描述环境和场景的数据，这些数据是模拟器、未遂事件和专家启发数据无法完全实现的[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（例如从模拟器和从相似处学习）获得额外信息。该方法可以最大限度地减少专家对人为错误概率定义的判断，评估不同情况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.