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Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

机译：使用智能代理对紧急疏散过程中的人类行为进行建模：一种多代理仿真方法

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It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.
机译：灾难员工需要花费大量时间并花费大量时间为建筑物执行疏散演练。如果不进行大量演练，就无法收集信息以推进即将到来的建筑物的计划和蓝图。我们已经开发了一种多智能体系统仿真应用程序，以帮助运行多个疏散演习和理论情况。本文将遗传算法（GA）与神经网络（NN）和模糊逻辑（FL）结合起来，探讨智能特工如何在疏散过程中学习和适应其行为。适应性行为侧重于特定代理更改其在环境中的行为。代理的共享行为强调了多代理系统中的人群建模和紧急行为。本文提供了一个模糊的个体模型，用于在正常和紧急情况下对人类情绪行为进行逼真的建模。它探讨了感知和情感对人类行为的影响。我们已经建立了一种具有独立性，集体能力，合作性和学习性等特征的新型智能代理，它描述了其最终行为。本文的贡献在于我们利用GA，NN和FL在多智能体系统中对智能体的学习和适应行为进行建模的方法。计划中的应用程序将通过整合探员特征，针对社交和文化问题（如疏散）的假设情景，帮助执行大量疏散演练。本文还将我们提出的多主体系统与现有的商业疏散工具以及实时疏散演练进行了比较，以确保准确性，建筑物交通特征以及疏散过程中离开的累计人数。我们的结果表明，GA，NN和模糊属性的包含使特工的疏散时间更接近实时疏散演练。

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