Disaster management in real time simulation using machine learning
Disaster management in real time simulation using machine learning
复制标题
使用机器学习进行实时模拟的灾害管理
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
J. Martí
中科院分区：
文献类型：
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作者：
Mohammed Talat Khouj;C. Castellanos;Sarbjit S. Sarkaria;J. Martí
在灾害期间，应急响应者精心选择的一系列决定对于减轻人员生命损失和恢复关键基础设施至关重要。在本文中，我们提出了协助人类应急响应建模和模拟智能代理使用强化学习。代理人的目标将是最大限度地增加从医院或现场急救单位出院的患者数量。有人建议，通过暴露这样一个智能代理到一个大的模拟灾难场景序列，代理将捕获足够的经验和知识，使其能够选择那些行动，减轻损害和伤亡。本文介绍了我们的工作的早期结果表明，使用Q学习可以成功地训练代理，使好的选择，在模拟灾难。
A series of carefully chosen decisions by an Emergency Responder during a disaster are vital in mitigating the loss of human lives and the recovery of critical infrastructures. In this paper we propose to assist a human Emergency Responder by modeling and simulating an intelligent agent using Reinforcement Learning. The goal of the agent will be to maximize the number of patients discharged from hospitals or on-site emergency units. It is suggested that by exposing such an intelligent agent to a large sequence of simulated disaster scenarios, the agent will capture enough experience and knowledge to enable it to select those actions which mitigate damage and casualties. This paper describes early results of our work that indicate that the use of Q-learning can successfully train an agent to make good choices, during a simulated disaster.