On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities

• URL: http://arxiv.org/abs/2001.07362v3
• Date: Thu, 12 Mar 2020 00:12:20 GMT
• Title: On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
• Authors: Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Yevgeniy Vorobeychik, Abhishek Dubey
• Abstract summary: Emergency Response Management (ERM) is a critical problem faced by communities across the globe. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. We propose two partially decentralized multi-agent planning algorithms that utilizes and exploit the structure of the dispatch problem.
• Score: 21.22596396400625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [49.2719253711215]
  This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM) Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM. This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
  arXiv  Detail & Related papers  (2024-06-16T23:01:10Z)
• Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing [8.293120269016834]
  An emergency responder management (ERM) system dispatches responders when it receives requests for medical aid. ERM systems can proactively reposition responders between predesignated waiting locations to cover any gaps. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search. We introduce a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning.
  arXiv  Detail & Related papers  (2024-05-21T21:15:45Z)
• Artificial Intelligence for Emergency Response [0.6091702876917281]
  Emergency response management (ERM) is a challenge faced by communities across the globe. Data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch.
  arXiv  Detail & Related papers  (2023-06-15T18:16:08Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• Designing Decision Support Systems for Emergency Response: Challenges and Opportunities [3.8532022064807827]
  Emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with community partners.
  arXiv  Detail & Related papers  (2022-02-23T02:02:32Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)
• Contingency-Aware Influence Maximization: A Reinforcement Learning Approach [52.109536198330126]
  influence (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms.
  arXiv  Detail & Related papers  (2021-06-13T16:42:22Z)
• Hierarchical Planning for Resource Allocation in Emergency Response Systems [0.8602553195689513]
  A classical problem in city-scale cyber-physical systems is resource allocation under uncertainty. Online, offline, and decentralized approaches have been applied to such problems, but 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.
  arXiv  Detail & Related papers  (2020-12-24T15:55:23Z)
• Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources [61.75759893720484]
  Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources.
  arXiv  Detail & Related papers  (2020-07-13T03:51:08Z)
• Improving Community Resiliency and Emergency Response With Artificial Intelligence [0.05541644538483946]
  We are working towards a multipronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first.
  arXiv  Detail & Related papers  (2020-05-28T18:05:08Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.