Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder \textit{after} incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.
MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Electric Power Research Institute（电力科学研究院）
AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control
Center for Applied Intelligent Systems Research, Halmstad University, Sweden（哈尔姆斯塔德大学应用智能系统研究中心, 瑞典）；RISE Research Institutes of Sweden, Göteborg, Sweden（瑞典RISE研究机构, 哥德堡, 瑞典）
From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV
Real-Time and Embedded Computing Systems Research Centre (CISTER)（实时嵌入式计算系统研究中心）；Carnegie Mellon University（卡内基梅隆大学）；Pontificia Universidad Católica de Chile（天主教大学）；School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；Instituto de Telecomunicações, Faculdade de Engenharia, Universidade do Porto（电信研究所，工程学院，葡萄牙里斯本大学）；University of Houston（休斯顿大学）
From Large-scale Audio Tagging to Real-Time Explainable Emergency Vehicle Sirens Detection
Department of Information Engineering, Computer Science and Mathematics (DISIM), University of L’Aquila（信息工程、计算机科学与数学系（DISIM），拉奎拉大学）；National Inter-University Consortium for Telecommunications (CNIT), University of L’Aquila（电信国家大学间联合体（CNIT），拉奎拉大学）
