MULTI-AGENT SYSTEM FOR DISASTER RESPONSE AND RECOVERY

Abstract

Natural and man-made disasters such as floods, earthquakes, cyclones, and wildfires pose significant threats to human life, infrastructure, and the environment. Effective disaster response and recovery require timely decision-making, efficient coordination among multiple agencies, and rapid resource allocation. However, traditional disaster management systems often suffer from delays, lack of coordination, and inefficient communication, which can lead to increased damage and loss of life. With the advancement of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI), there is a growing opportunity to enhance disaster management systems by introducing intelligent, autonomous, and collaborative solutions. This project presents a GenAI-powered Multi-Agent System for Disaster Response and Recovery, designed to simulate the coordination of multiple intelligent agents working together in disaster scenarios. A multi-agent system consists of several autonomous agents, each responsible for performing a specific task while interacting with other agents to achieve a common goal. In the context of disaster management, this approach enables parallel processing, faster decision-making, and improved efficiency compared to centralized systems. The proposed system includes multiple specialized agents such as an Alert Agent, Rescue Agent, Medical Agent, Transport Agent, and Communication Agent. The Alert Agent is responsible for detecting disaster-related information from input data or simulated sources. Once a disaster is identified, the Rescue Agent analyzes the affected regions and identifies critical locations that require immediate attention. The Medical Agent provides recommendations for nearby hospitals and medical facilities, ensuring that injured individuals receive timely care. The Transport Agent determines safe and optimal routes for rescue operations, avoiding hazardous areas. The Communication Agent disseminates alerts and updates to relevant authorities and affected individuals, ensuring effective information flow.