Resilience learning through self adaptation in digital twins of human-cyber-physical systems

E. Bellini, F. Bagnoli, M. Caporuscio, E. Damiani, Francesco Flammini, I. Linkov, P. Lio’, S. Marrone
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引用次数: 5

Abstract

Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and implications, which in turn require the adoption of advanced monitoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms, data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation to time-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.
在人-网络-物理系统的数字孪生体中通过自我适应的弹性学习
人-网络-物理系统(HPCS)作为现代社会的关键基础设施,由于其复杂的相互联系和相互依赖关系,受到多种系统性威胁。系统威胁的管理需要从静态风险评估到使用智能、数据驱动和运行时方法的整体弹性建模和评估的范式转变。事实上,HCPS的复杂性和重要性要求及时做出决策,考虑到许多参数和影响,这反过来又要求采用先进的监测框架和评估工具。为了应对这一挑战,我们在一个名为RESILTRON的框架中引入了这些新范式,设想数字双胞胎(DT)来支持HCPS在系统压力下的决策和提高弹性。为了表示可能复杂和异构的HCPS及其环境和压力源,我们利用多仿真方法,通过结构化,模块化和组合框架,结合多种形式化,数据驱动方法和人工智能(AI)建模范式。利用DT为系统提供多层空间嵌入动态网络的自适应抽象表示,并将自适应应用于时间扭曲的假设分析,以找到最佳决策序列,以确保在不确定性和连续HPCS进化下的弹性。
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