A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized approaches have been applied to such problems, they have difficulty scaling to large decision problems. We present a general approach to hierarchical planning that leverages structure in city-level CPS problems for resource allocation under uncertainty. We use the emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from Nashville, Tennessee, a major metropolitan area in the United States, to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue
College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所）
Beijing University of Posts and Telecommunications（北京邮电大学）；Massachusetts Institute of Technology（麻省理工学院）；IFLYTEK Co., Ltd（科大讯飞股份有限公司）
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time…
Stochastic sequential decision making often requires hierarchical structure in the problem where each high-level action should be further planned with primitive states and actions. In addition, many real-world applications require a plan that satisfies constraints on the secondary costs such as risk measure or fuel consumption. In this paper, we propose a hierarchical constrained stochastic shortest path problem (HC-SSP) that meets those two crucial requirements in a single framework. Although HC-SSP provides a useful framework to model such planning requirements in many real-world application…
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning, which provides more detailed information about individual robot behaviour, but also exploits independence between tasks to do so efficiently. We use Markov decision processes to model robot behaviour and linear temporal logic to specify tasks and safety constraints. Building upon techniques and tools from formal verification, we show how to generate a sequence of multi-robot policies, iteratively refining them to rea…
Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）；Frontier Research Center, Peng Cheng Laboratory（鹏城实验室前沿研究中心）
A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Advanced Institute of Big Data (Beijing)（北京大数据研究院）
Real-world multi-agent planning problems cannot be solved using decision-theoretic planning methods due to the exponential complexity. We approximate firefighting in rescue simulation as a spatially distributed task and model with multi-agent Markov decision process. We use recent approximation methods for spatial task problems to reduce the model complexity. Our approximations are single-agent, static task, shortest path pruning, dynamic planning horizon, and task clustering. We create scenarios from RoboCup Rescue Simulation maps and evaluate our methods on these graph worlds. The results sh…
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 respon…
Stanford University（斯坦福大学）；Vanderbilt University（范德堡大学）；DataMinr；Tennessee Department of Transportation（田纳西州交通部）；Washington University at St. Louis（圣路易斯华盛顿大学）
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, an…
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities. Emergency response management comprises of several stages and sub-problems like forecasting, resource allocation, and dispatch. The design of principled approaches to tackle each problem is necessary to create efficient emergency response management (ERM) pipelines. Over the last six years, we have worked with several first responder organizations to design ERM pipelines. In this paper, we highlight some of the challenges that we have identified and lessons that we have learned through…
University of North Texas（北德克萨斯大学）；Georgia State University（佐治亚州立大学）
The delivery of Medical Countermeasures(MCMs) for mass prophylaxis in the case of a bio-terrorist attack is an active research topic that has interested the research community over the past decades. The objective of this study is to design an efficient algorithm for the Receive Reload and Store Problem(RSS) in which we aim to find feasible routes to deliver MCMs to a target population considering time, physical, and human resources, and capacity limitations. For doing this, we adapt the p-median problem to the POD-based emergency response planning procedures and propose an efficient algorithm …
Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departments
John Bapst Memorial High School（约翰·巴普斯特纪念高中）；Minerva University（密涅瓦大学）
Monash University（莫纳什大学）；Turning Point, Eastern Health Clinical School, Monash University（莫纳什大学东方健康临床学院Turning Point）
Emergency Medical Services (EMS) demand load has become a considerable burden for many government authorities, and EMS demand is often an early indicator for stress in communities, a warning sign of emerging problems. In this paper, we introduce Deep Planning and Policy Making Net (DeepPPMNet), a Long Short-Term Memory network based, global forecasting and inference framework to forecast the EMS demand, analyse causal relationships, and perform `what-if' analyses for policy-making across multiple local government areas. Unless traditional univariate forecasting techniques, the proposed method …
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsy…
Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genoa（热那亚大学信息学、生物工程、机器人与系统工程系）
We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in…
Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin
Constructor University（Constructor大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Asklepios Kliniken Hamburg GmbH（阿斯克列庇斯汉堡医院有限公司）
This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to modeling and integrating dynamical uncertainties into a safe planning framework, and finding a solution in a computationally tractable way. In this work, we first develop a probabilistic model for dynamical uncertainties. Then, we provide a framework to generate a path that maximizes safety for complex missions by incorporating the uncertainty model. We also devise a Monte Car…
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multipronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information…
RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University（北京未来区块链与隐私计算先进创新中心，人工智能学院，北航）；Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Center for Applied Statistics, School of Statistics, Renmin University of China（应用统计中心，统计学院，中国人民大学）
Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown an…
