## 6 Conclusions  
The modus operandi of modern urban communities has been profoundly and increasingly reliant upon the enduring functionality supply from a diverse array of CISs; however, real-world hazard events have consistently revealed the vulnerabilities of these CISs in communities across the spectrum of wealth. Over the past two decades, extensive research has sought to establish resilient CISs capable of withstanding such events while swiftly recovering functionality. Notably, this research endeavour provides valuable insights into the impact of interdependencies among CISs. These intricate connections play a pivotal role in amplifying initial local damage or dysfunction on a global scale. Additionally, state-of-the-art assessment frameworks consider not only the physical resilience of networked CISs but also their multidimensional resilience from a socio-economic perspective. Despite these significant advances, recent extreme climate events have exposed the limitations of these frameworks, leading to the following conclusions:The majority of the state-of-the-art resilience assessment frameworks indiscriminately characterize the criticality across different temporal windows during hazard events. Conversely, damage or dysfunction initiated by a hazard in any single CIS tends to propagate, potentially triggering a full-blown cascading failure in the immediate aftermath of such events;  
In this context, post-shock emergency responses emerge as a promising pathway toward resilient urban communities. These responses aim to swiftly restore the functionality of hazard-impacted CISs to a minimal yet acceptable level immediately after shocking events. By effectively inhibiting potential cascading failures triggered by the initial damage, these emergency responses facilitate smoother long-term recovery;  
Practical implementation of emergency responses faces obstacles from computational, technical, and socio-economic perspectives;  
Pilot studies have utilized advanced machine learning techniques to enhance post-shock emergency responses of Critical Infrastructure Systems (CISs). These efforts underscore AI-driven strategies that can rapidly restore functionality and bolster resilience in the aftermath of disasters. Notably, deep learning has proven to be a powerful tool for swift damage assessment immediately following hazardous events. Progress in this field has established the groundwork for adaptive post-shock emergency responses, with (deep) reinforcement learning playing a crucial role;  
By integrating state-of-the-art AI with robotics engineering, autonomous, grouped, and humanoid recovery robots could potentially revolutionize urban crisis management. These robots not only shield human responders from hazard-induced dangers but also devise strategies that may surpass traditional top-down, experience-based protocols. Lastly, this paper also serves as a cautionary message to the scientific community and public regarding the potential risks associated with the convergence of AI and emergency responses. Amidst the intricate and dynamic interactions within urban communities throughout hazard events, any erroneous prediction or unsafe action by these AI agents could inadvertently exacerbate the damage sustained by hazard-impacted CISs, potentially exceeding the initial hazards themselves; furthermore, decision-making by these AI agents during emergency response campaigns may unintentionally perpetuate societal inequality across hazard-affected communities. Without vigilant oversight, their actions could unwittingly exacerbate existing disparities; therefore, comprehensive studies are imperative to regulate the behaviour of AI agents, ensuring alignment with ethical norms while contributing to the resilience of future urban communities imperilled by various natural and/or man-made catastrophes. Conceptualization, L.S., H.L. and J.N. ; Investigation, L.S., J.N. and S.Y. ; Writing--original draft, L.S. and J.N. ; Writing--review & editing, S.Y. All authors have read and agreed to the published version of the manuscript.