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

Author

Listed:
  • Sharad Sharma

    (Bowie State University)

  • Kola Ogunlana

    (Bowie State University)

  • David Scribner

    (Army Research Laboratory, HRED, APG)

  • Jock Grynovicki

    (Army Research Laboratory, HRED, APG)

Abstract

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.

Suggested Citation

  • Sharad Sharma & Kola Ogunlana & David Scribner & Jock Grynovicki, 2018. "Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach," Information Systems Frontiers, Springer, vol. 20(4), pages 741-757, August.
  • Handle: RePEc:spr:infosf:v:20:y:2018:i:4:d:10.1007_s10796-017-9791-x
    DOI: 10.1007/s10796-017-9791-x
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    Citations

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    Cited by:

    1. Ghassan Beydoun & Sergiu Dascalu & Dale Dominey-Howes & Andrew Sheehan, 2018. "Disaster Management and Information Systems: Insights to Emerging Challenges," Information Systems Frontiers, Springer, vol. 20(4), pages 649-652, August.
    2. Ling Tan & Ji Guo & Selvarajah Mohanarajah & Kun Zhou, 2021. "Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2389-2417, July.
    3. Karel Mls & Milan Kořínek & Kamila Štekerová & Petr Tučník & Vladimír Bureš & Pavel Čech & Martina Husáková & Peter Mikulecký & Tomáš Nacházel & Daniela Ponce & Marek Zanker & František Babič & Ioanna, 2023. "Agent-based models of human response to natural hazards: systematic review of tsunami evacuation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 1887-1908, February.

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