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

Sharma, Sharad; Ogunlana, Kola; Scribner, David R.; Grynovicki, Jock O.

doi:10.1007/s10796-017-9791-x

Cited by 44 publications

(15 citation statements)

References 16 publications

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“…Repetitive interactions are hard to implement with mathematical methods, which in contrast is a peculiarity of agent-based simulation. Agent-based simulation is widely used in many fields to deal with problems covered in complex systems [48], like resource allocation [49], crowd evacuation [50], decision-making systems [51], and cooperative game [52]. e application of agent-based simulation in disaster management can improve search and rescue efficiency [53].…”

Section: Methodsmentioning

confidence: 99%

MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster

et al. 2020

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The frequent occurrence of urban waterlogging constantly affects resident living and urban construction. Improved adaptive prevention and control strategies are highly requested due to huge economic losses and casualties caused by flood and waterlogging in China. The urban waterlogging may evolve into a serious emergency, generally characterized by high complexity, uncertainty, and time pressure. Coupled with the asymmetric information, waterlogging often exacerbates the impact of urban rainstorm disasters. Through the multi-agent system simulation with given geographic information, government and residents interact under dynamic risk distribution in rainstorm disaster. The results show that the proactive attitude of residents and the government towards disaster relief could have a promoting effect for both, thereby increasing the disaster relief efficiency. Obviously, rapid accurate information collection and analysis facilitate disaster relief to a large extent. Meanwhile, appropriate supply rather than excessive supply may mobilize residents’ self-help and balance replenishment of relief supplies.

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Section: Methodsmentioning

confidence: 99%

MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster

et al. 2020

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“…To address these limitations, different approaches, e.g., (Sharma et al 2018), make use of Bayesian networks (Pearl 1994;Darwiche 2009), or Causal networks (Pearl 2000;Pearl & Mackenzie 2018) to model the behaviour of agents. These networks can implement the decision-making process conducted by the agents to decide the next action to perform.…”

Section: Modeling the Behavior Of Agentsmentioning

confidence: 99%

Modeling Objects with Uncertain Behaviors.

Muñoz

Karkhanis

Brand

et al. 2021

JOT

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Modeling the behavior of complex systems that operate in real environments, deal with physical elements, or interact with humans is a challenging task. It involves the explicit representation of aspects of behavioral uncertainty that are inherent in the system, but generally neglected in software models. In this paper, we focus on the explicit representation of the behavior of objects of complex systems, considering their motivations, randomness, and the different types of underlying uncertainty that affect their actions. We show how such uncertain behaviors can be effectively modeled in UML and OCL, and how the specifications produced can be used to simulate and analyze these systems.

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“…Paper 6 by Sharma et al (2018) uses a multi-agent system simulation to predict and adjust evacuation procedures. The paper acknowledges the fuzzy and collective nature of disaster response actions.…”

Section: Stakeholder Analysismentioning

confidence: 99%

“…It tackles the problem of how to best analyze the requirements in a way to identify system needs. It promotes the use of agent oriented analysis not only to support applications such as those presented in Sharma et al (2018) and Kamyabniya et al (2018), but to also manage DM knowledge sources more broadly. Such knowledge sources often describe stakeholders, their roles and constraints in DM.…”

Section: Stakeholder Analysismentioning

confidence: 99%