Multi-Agent Simulation Collision Avoidance of Complex System

Chennoufi, Mohammed; Bendella, Fatima; Bouzid, Maroua

doi:10.4018/ijaci.2018010103

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…One of the studies use NetLogo belong to Chennoufi et al (2018). They proposed a collision avoidance technique for evacuation of individuals which was tested by a simulation developed in NetLogo.…”

Section: Netlogomentioning

confidence: 99%

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Cenani

2021

Journal of Computational Design

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Agent-based systems are an important application area of artificial intelligence and are used in decision support systems. Rather than being a problem-solving tool, agent-based system is a tool for developing and testing alternative solutions according to various scenarios. In this context, agent-based modeling is a very effective method to support decision makers in emergency situations to evaluate different risk scenarios and then make decisions quickly and effectively. Moreover, agent-based modeling is a very useful method to support decision makers in situations of high complexity and uncertainty. This paper introduces current studies performed with several agent-based modeling tools and software environments such as NetLogo, AnyLogic, MATSim and Repast. Apart from these, various agent-based modeling tools exist, but these four tools have been chosen because they are still receiving software updates and being widely used in the most current studies. The aims of this study are to review these four agent-based modeling tools, present state-of-the-art research conducted with these tools and provide a reference of agent-based modeling tools for researchers who are developing decision support systems in architectural, urban and transportation design research fields. In this paper, after giving a brief definition of an agent-based system and explaining the importance of concepts such as emergence and complexity in the field of agent-based modeling, it is explained who uses the agentbased models for what purpose, when, where, why and how to use agent-based modeling through selected examples from state-of-the-art studies carried out in different research fields. Furthermore, what current studies and agent-based modeling tools teach us and how future studies can benefit from agent-based models are briefly discussed.

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Section: Netlogomentioning

confidence: 99%

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Cenani

2021

Journal of Computational Design

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“…Fu et al 30 obtained better results by integrating multiagent and cellular automation models. In Reference 31, a multiagent simulation collision avoidance system in a complex environment was proposed, and its application to crowd evacuation behavior was presented. Additionally, a computation framework to simulate human and social behaviors for egress analysis was presented by Pan et al; 32 the MASSEgress system was proposed to conduct simulation based on K‐Means clustering analysis; however, the number of agents is less than 100, and it mainly aimed to simulate indoor scenarios, such as train stations, buildings, and so forth.…”

Section: Related Workmentioning

confidence: 99%

Multiagent evacuation framework for a virtual fire emergency scenario based on generative adversarial imitation learning

Zhou

Wen-ying

Jie

et al. 2021

Computer Animation & Virtual

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One of the most common solutions for the prevention of fire accidents is to conduct extensive fire evacuation drills in crowded places. However, there are multiple salient advantages to using virtual reality technology to simulate emergency solutions, for instance, saving costs and greatly decreasing uncertain risks or accidents. Therefore, in this article, a multiagent evacuation framework for complex virtual fire scenarios is proposed and effectively used to simulate a multiagent evacuation procedure to approximate the goal of fire drills in a less costly manner. Specifically, the concept of a multihierarchy agent group model is proposed; that is, the evacuation of multiple agents is separated into leader‐follower and freedom modes. Additionally, several complex actions of individual humans in actual fire drills are fully considered, and a multiaction agent schema is presented to characterize the associated real effects. In addition, generative adversarial imitation learning is adopted to obtain the evacuation path of the leader‐agent by training numerous learning epochs. Finally, extensive experiments are conducted to validate the feasibility of our proposed method. The results show that the proposed method is superior to other methods and that it realistically and reasonably shows the procedure of multiagent evacuation in complex fire emergency scenarios.

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“…In a stochastic delayed MAS with nonlinear dynamics where the system switched asynchronously, Wu et al investigated the distributed exponential consensus. [22]. This part [23] will discuss using MASs to create workflows aware of their context.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

Begari,

Reddy,

Sujatha

2023

IJEER

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Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability. The control agents of Distributed Generations (DGs) are optimized using GWO algorithm based on the deviations in frequencies of DGs. Particle Swarm Optimization based Multi Agent System (PSO-MAS) and Conventional Multi Agent System performance are compared with the suggested technique's implementation on IEEE 30 bus system and testing in MATLAB/Simulink environment. The outcomes showed that the suggested approach is successfully improving the stability of an interconnected power system.

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Multi-Agent Simulation Collision Avoidance of Complex System

Cited by 14 publications

References 16 publications

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Multiagent evacuation framework for a virtual fire emergency scenario based on generative adversarial imitation learning

Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

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