scholarly journals Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1353
Author(s):  
Hai Sun ◽  
Lanling Hu ◽  
Wenchi Shou ◽  
Jun Wang
Keyword(s):  
Quantitative Relation ◽  
Response Patterns ◽  
Multi Agent Systems ◽  
Systems Methodology ◽  
Cellular Automata Model ◽  
Self Organized ◽  
Multi Agent ◽  
Exit Choice ◽  
Evacuation Plan ◽  
Exit Options

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.

scholarly journals Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2642
Author(s):  
Godwin Asaamoning ◽  
Paulo Mendes ◽  
Denis Rosário ◽  
Eduardo Cerqueira
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Cooperative Behavior ◽  
Networked Control ◽  
Multi Agent Systems ◽  
Wireless Communication Network ◽  
Self Organized ◽  
Design Choice ◽  
Multi Agent ◽  
Computational Systems

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

scholarly journals Determining asymptotically large population sizes in insect swarms

2014 ◽  
Vol 11 (99) ◽  
pp. 20140710 ◽  
Author(s):  
James G. Puckett ◽  
Nicholas T. Ouellette
Keyword(s):  
Group Dynamics ◽  
Large Population ◽  
Environmental Cues ◽  
Multi Agent Systems ◽  
Strong Constraint ◽  
Agent Systems ◽  
Self Organized ◽  
Large Numbers ◽  
Population Sizes ◽  
Multi Agent

Social animals commonly form aggregates that exhibit emergent collective behaviour, with group dynamics that are distinct from the behaviour of individuals. Simple models can qualitatively reproduce such behaviour, but only with large numbers of individuals. But how rapidly do the collective properties of animal aggregations in nature emerge with group size? Here, we study swarms of Chironomus riparius midges and measure how their statistical properties change as a function of the number of participating individuals. Once the swarms contain order 10 individuals, we find that all statistics saturate and the swarms enter an asymptotic regime. The influence of environmental cues on the swarm morphology decays on a similar scale. Our results provide a strong constraint on how rapidly swarm models must produce collective states. But our findings support the feasibility of using swarms as a design template for multi-agent systems, because self-organized states are possible even with few agents.

scholarly journals Using Agent-Based Methodologies in Healthcare Information Systems

2018 ◽  
Vol 18 (2) ◽  
pp. 123-132 ◽  
Author(s):  
Reem Abdalla ◽  
Alok Mishra
Keyword(s):  
Information Systems ◽  
Systems Engineering ◽  
Open Systems ◽  
Health Information System ◽  
Multi Agent Systems ◽  
Health Care Information ◽  
Systems Methodology ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent

Abstract This paper carries out a comparative analysis to determine the advantages and the stages of two agent-based methodologies: Multi-agent Systems Engineering (MaSE) methodology, which is designed specifically for an agent-based and complete lifecycle approach, while also being appropriate for understanding and developing complex open systems; Agent Systems Methodology (ASEME) suggests a modular Multi-Agent System (MAS) development approach and uses the concept of intra-agent control. We also examine the strengths and weaknesses of these methodologies and the dependencies between their models and their processes. Both methodologies are applied to develop The Guardian Angle: Patient-Centered Health Information System (GA: PCHIS), which is an example of agent-based applications used to improve health care information systems.

scholarly journals Self-organized division of cognitive labor

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254532
Author(s):  
Edgar Andrade-Lotero ◽  
Robert L. Goldstone
Keyword(s):  
Computational Model ◽  
Division Of Labor ◽  
Group Dynamics ◽  
Clear Understanding ◽  
Multi Agent Systems ◽  
Cognitive Mechanisms ◽  
Self Organized ◽  
Potential Applications ◽  
Multi Agent ◽  
Person Game

Often members of a group benefit from dividing the group’s task into separate components, where each member specializes their role so as to accomplish only one of the components. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and communication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an iterative two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the perceived similarity between a player’s actions and the task’s focal points guided the players’ choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration.

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