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In this paper, we propose a method to solve the optimization problem in an emergency evacuation. Specifically, when passengers are waiting for their flight in the lounge and a fire breaks out. How to evacuate all passengers to a safe place with the minimum total evacuation time? To solve the problem, we propose a method based on the multi-agent multi-level model MAS-GiG [1], combined with the shortest path algorithm to guide passengers to evacuate to a safe place. Additionally, we address issues that arise during the evacuation process, such as reducing the speed of groups when two or more groups collide, and changing the evacuation plan when the routes to the exit are blocked. We compare the proposed method with the method in [2] to provide specific evaluations and future research directions. The testing environment is the departure hall, 1st floor of Danang International Airport.

Hành vi của con người ở những nơi công cộng thường rất phức tạp, luôn thay đổi và chịu tác động của nhiều yếu tố. Trong trường hợp khẩn cấp như hỏa hoạn, hành vi của con người trở nên hỗn loạn và không kiểm soát được. Có nhiều lĩnh vực nghiên cứu về hành vi của đám đông như: Cellular Automata, Social Force, Fluid-dynamic, dựa vào tác tử, lý thuyết trò chơi. Tuy nhiên, mỗi phương pháp đều có ưu và nhược điểm riêng. Trong bài báo này, nhóm tác giả đề xuất phương pháp mô hình hóa hành vi đám đông dựa vào mô hình đa tác tử MAS-GiG [1]. Nhóm tác giả dựa trên nền tảng mô phỏng MASH [2] để thiết kế và xây dựng framework cho ứng dụng. Áp dụng phương pháp đề xuất [3] hướng dẫn hành khách sơ tán khi có cháy. Để đánh giá phương pháp đề xuất, nhóm tác giả so sánh phương pháp thực hiện và kết quả thực nghiệm giữa phương pháp đề xuất và phương pháp sơ tán tự động. Môi trường thực nghiệm gồm phòng chờ số 5 và 6, ga đi, sân bay quốc tế Đà Nẵng, Việt Nam.

Emergency evacuation is of paramount importance in protecting human lives and property while enhancing the effectiveness and preparedness of organizations and management agencies in responding to emergencies. In this paper, we propose a method for evacuating passengers to safe places with the shortest possible evacuation time. The proposed method is based on a multi-level multi-agent MAS-GiG model combined with three problems. First, constructing a path map to select the shortest path; second, dividing the space of the experimental environment into smaller areas for efficient management, monitoring, and guiding evacuation; the third, adjusting the speed to handle collision issues and maintain distance between two or more groups of evacuees while moving. We extend our previous study by establishing groups based on the location of passengers and using a MAS-GiG model to guide evacuation. We compare the proposed method with our previous method to provide specific evaluations for the research and research in the future. We tested two methods in the departure hall, first floor, Danang International Airport, Vietnam.

A cyber-physical system (CPS) is a system with integrated computational and physical abilities. Deriving the notion of cyber-physical collective (CPC) from a social view of CPS, we consider the nodes of a CPS as individuals (agents) that interact to overcome their limits in the collective. When CPC agents are able to move in their environment, the CPC is considered as a Mobile CPC (MCPC). The interactions of the agents give rise to the appearance of a phenomenon collectively generated by the agents of the CPC that we call a collective product. This phenomenon is not recorded as “a whole” in the CPC because an agent has only a partial view of its environment. This paper presents COPE (COllective Product Exploitation), an approach that allows one MCPC to exploit the collective product of another one. The approach is based on the deployment of meta-agents in both systems. A meta-agent is an agent that is external to a MCPC but is associated with one of its agents. Each meta-agent is able to monitor the agent with which it is associated and can fake its perceptions to influence its behavior. The meta-agents deployed in the system from which the collective product emerges provide indicators related to this product. Utilizing these indicators, the meta-agents deployed in the other system can act on the agents in order to adapt the global dynamics of the whole system. The proposed coupling approach is evaluated in a “fire detection and control” use case. It allows a system of UAVs to use the collective product of a network of sensors to monitor the fire.