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Готові списки джерел за темами / Cooperative Multi-agent system / Статті в журналах

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Статті в журналах з теми "Cooperative Multi-agent system"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

BENOUHIBA, T., and J. NIGRO. "An evidential cooperative multi-agent system." Expert Systems with Applications 30, no. 2 (2006): 255–64. http://dx.doi.org/10.1016/j.eswa.2005.07.008.

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2

Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.

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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case &ndash; by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a

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3

Ammar, Abdul Ameer Rasheed, Najm Abdullah Mohammed, and Sabah Al-Araji Ahmed. "A review of multi-agent mobile robot systems applications." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3517–29. https://doi.org/10.11591/ijece.v12i4.pp3517-3529.

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A multi-agent robot system (MARS) is one of the most important topics nowadays. The basic task of this system is based on distributive and cooperative work among agents (robots). It combines two important systems; multi-agent system (MAS) and multi-robots system (MRS). MARS has been used in many applications such as navigation, path planning detection systems, negotiation protocol, and cooperative control. Despite the wide applicability, many challenges still need to be solved in this system such as the communication links among agents, obstacle detection, power consumption, and collision avoi

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4

YUAN, SOE-TSYR, and ZENG-LUNG WU. "AN INFRASTRUCTURE FOR ENGINEERING COOPERATIVE AGENTS." International Journal of Software Engineering and Knowledge Engineering 10, no. 06 (2000): 681–711. http://dx.doi.org/10.1142/s0218194000000377.

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Currently, systems of cooperative agents (multi-agent systems), possessing the capabilities of autonomy, adaptation, and cooperation, are being used in an increasingly wide variety of application areas, and the conversation-based multi-agent system design is the major design for those multi-agent systems. Supposedly, conversation-based multi-agent systems should have been prevailing enough for tackling dynamic aspects of problems in a variety of domains. However, for industries, multi-agent systems are still found to be in the birth stage where they only show their new values in anticipation f

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5

TACHIBANA, Yoshihiro, Syohei MORI, and Toru NAMERIKAWA. "Cooperative Control of Multi-agent System with Disturbance." Transactions of the Society of Instrument and Control Engineers 50, no. 9 (2014): 669–76. http://dx.doi.org/10.9746/sicetr.50.669.

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6

Allioui, Hanane, Mohamed Sadgal, and Aziz El Fazziki. "An Improved Image Segmentation System." Journal of communications software and systems 16, no. 2 (2020): 143–55. http://dx.doi.org/10.24138/jcomss.v16i2.830.

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In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation proce

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7

Liang, Along. "Multi-Agent-Based Film Editing Collaboration System." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–6. http://dx.doi.org/10.1155/2022/1327620.

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In order to realize the effective cooperation between editor agents in the film and television editing collaboration system, it is analyzed that the state change of the film and television editing and production process is affected by the cross influence of multiple factors. A single agent can no longer satisfy the current film and television production. From the point of view of system theory, this article constructs the learner agent in the film and television editing system by introducing a new cooperation mechanism—the multi-agent collaborative system model. Collaboration and cooperation b

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8

Oliveira, Eugénio, R. Camacho, and C. Ramos. "A multi-agent environment in robotics." Robotica 9, no. 4 (1991): 431–40. http://dx.doi.org/10.1017/s0263574700000618.

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SUMMARYThe use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks.Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while t

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9

Li, Na, and Yi Guo. "A Model for Cooperative Design Based on Multi-Agent System." Applied Mechanics and Materials 65 (June 2011): 160–64. http://dx.doi.org/10.4028/www.scientific.net/amm.65.160.

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Distributed cooperative design is carried out by teams located at different places. The regional limitation must be overcome to facilitate information exchange, knowledge processing, and design result exchange, etc., among the teams. This paper proposes a multi-agent based model for cooperative design. The model consists of five types of agents according to cooperative design environment and design activities. Integrated fine grained security mechanism into different agents is the major feature of this model.

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10

Elhachimi, Jamal, and Zouhair Guenoun. "Distributed Frequency Assignment Using Hierarchical Cooperative Multi-Agent System." International Journal of Communications, Network and System Sciences 04, no. 11 (2011): 727–34. http://dx.doi.org/10.4236/ijcns.2011.411089.

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11

D'Ariano, Andrea, and Robin Hemelrijk. "DESIGNING A MULTI-AGENT SYSTEM FOR COOPERATIVE TRAIN DISPATCHING." IFAC Proceedings Volumes 39, no. 3 (2006): 369–74. http://dx.doi.org/10.3182/20060517-3-fr-2903.00198.

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12

Kadir, Herdawatie Abdul, and Mohd Rizal Arshad. "Decentralized Cooperative Communication Framework for Heterogeneous Multi Agent System." Journal of Communications 9, no. 2 (2014): 163–70. http://dx.doi.org/10.12720/jcm.9.2.163-170.

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13

Renjit, J. Arokia. "Distributed and cooperative multi-agent based intrusion detection system." Indian Journal of Science and Technology 3, no. 10 (2010): 1070–74. http://dx.doi.org/10.17485/ijst/2010/v3i10.2.

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14

Guo, Jinchao, Gang Hu, and Hongying Wang. "Ian-Based Cooperative Control Model for Multi-agent System." Information Technology Journal 10, no. 6 (2011): 1215–21. http://dx.doi.org/10.3923/itj.2011.1215.1221.

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15

Srinivasan, D., and M. C. Choy. "Cooperative multi-agent system for coordinated traffic signal control." IEE Proceedings - Intelligent Transport Systems 153, no. 1 (2006): 41. http://dx.doi.org/10.1049/ip-its:20055011.

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16

Oliveira, Eugénio. "Cooperative multi-agent system for an assembly robotics cell." Robotics and Computer-Integrated Manufacturing 11, no. 4 (1994): 311–17. http://dx.doi.org/10.1016/0736-5845(95)00008-9.

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17

Premarathne, U. S., and S. Rajasingham. "Trust based multi-agent cooperative load balancing system (TCLBS)." Future Generation Computer Systems 112 (November 2020): 185–92. http://dx.doi.org/10.1016/j.future.2020.01.037.

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18

Cena, Cecilia Garcia, Pedro F. Cardenas, Roque Saltaren Pazmino, Lisandro Puglisi, and Rafael Aracil Santonja. "A cooperative multi-agent robotics system: Design and modelling." Expert Systems with Applications 40, no. 12 (2013): 4737–48. http://dx.doi.org/10.1016/j.eswa.2013.01.048.

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19

Garcia, Cena Cecilia Elizabet, Herrera Pedro Fabián Cárdenas, Pazmiño Roque Jacinto Saltaren, Molina Lisandro José Puglisi, and Rafael Aracil. "A cooperative multi-agent robotics system: Design and modelling." Expert Systems with Applications 40, no. 12 (2013): 4737–48. https://doi.org/10.1016/j.eswa.2013.01.048.

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This paper presents the development of the robotic multi-agent system SMART. In this system, the agent concept is applied to both hardware and software entities. Hardware agents are robots, with three and four legs, and an IP-camera that takes images of the scene where the cooperative task is carried out. Hardware agents strongly cooperate with software agents. These latter agents can be classified into image processing, communications, task management and decision making, planning and trajectory generation agents. To model, control and evaluate the performance of cooperative tasks among agent

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20

Shih, Cyuansi, and Chengyu Cao. "Nonlinear Uncertainties Canceling in Multi-Agent Systems Enabled by Cooperative Adaptation." Journal of Control Science and Engineering 2018 (September 2, 2018): 1–9. http://dx.doi.org/10.1155/2018/3927108.

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This paper deals with uncertainties problem in multi-agent systems with novel cooperative adaptation approach. Since uncertainties in multi-agent systems are interconnected, local agent often faces uncertainties not only from itself but also from neighbors. The proposed approach is that a local agent estimates uncertainties from itself and neighboring agents and then changes control strategy. The uncertainties or the equivalences of neighbors can be estimated based on their available outputs; thus, the local agent can adapt to them to cancel out these effects. Stability analysis is also derive

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21

Rahim Ullah, Muhammad Saeed, Wajid Ali, Junaid Nazar, and Fakhra Nazar. "A Cooperative Heterogeneous Multi-Agent System Leveraging Deep Reinforcement Learning." Knowledge and Decision Systems with Applications 1 (March 30, 2025): 112–24. https://doi.org/10.59543/kadsa.v1i.13931.

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The agent plays a pivotal role in the multi-agent environment. Previous studies have primarily focused on homogeneous agent collaboration, but the approaches used in these studies cannot be directly applied to heterogeneous agent collaboration. There is a need for a model that supports collaboration among heterogeneous agents, as real-world systems consist of agents with diverse shapes, functions, and tasks. This study proposes a framework that uses visual and vector observations to facilitate collaboration among heterogeneous agents. The seaport environment is simulated for this study. Visual

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22

Zhang, Liping, and Guoshan Zhang. "Cooperative optimal control for descriptor multi-agent systems." IMA Journal of Mathematical Control and Information 37, no. 3 (2019): 935–52. http://dx.doi.org/10.1093/imamci/dnz035.

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Abstract This paper provides a theory analysis of cooperative optimal control problem for leader-follower descriptor multi-agent systems. Based on the linear quadratic regulator theory, the state feedback controller is designed to guarantee the consensus of multi-agent systems and minimize a local performance index, which is independent of the graph topology, the control gain matrix is obtained by solving a generalized Riccati equation. Because the local optimal protocol devised cannot guarantee the global optimality of the entire system, the inverse optimal control theory is applied to design

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23

Shao, Jing-Ping, and Yu-Ping Tian. "Cooperative source localisation of multi-agent system based on a cooperative PE condition." IET Control Theory & Applications 9, no. 1 (2015): 42–51. http://dx.doi.org/10.1049/iet-cta.2014.0049.

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24

DJEBRANI, SALIMA, FOUDIL ABDESSEMED, and ABDERRAOUF BENALI. "A MULTI-AGENT STRATEGY FOR A SIMPLE COOPERATIVE BEHAVIOR." International Journal of Information Acquisition 07, no. 04 (2010): 331–45. http://dx.doi.org/10.1142/s0219878910002257.

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This paper presents an application of the multi-agent system approach to mobile manipulators that cooperate to achieve displacement of heavy and large objects. Therefore, the task is distributed between agents, and each agent offers and negotiates its services that help in accomplishing the mission. The multi-agent system has been developed using the oRis platform. The main components of such a system include communication and negotiation protocols. Hence we present the behavior of robots, their interaction, and their behavior toward obstacles. Computer simulations are conducted in order to de

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25

Manickavalli, Subramanian, Arumugam Parivallal, Ramasamy Kavikumar, and Boomipalagan Kaviarasan. "Distributed Bipartite Consensus of Multi-Agent Systems via Disturbance Rejection Control Strategy." Mathematics 12, no. 20 (2024): 3225. http://dx.doi.org/10.3390/math12203225.

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This work aims to focus on analyzing the consensus control problem in cooperative–competitive networks in the occurrence of external disturbances. The primary motive of this work is to employ the equivalent input-disturbance estimation technique to compensate for the impact of external disturbances in the considered multi-agent system. In particular, a suitable low-pass filter is implemented to enhance the accuracy of disturbance estimation performance. In addition, a specific signed, connected, and structurally balanced undirected communication graph with positive and negative edge weights is

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26

DU, WEN-BO, XIAN-BIN CAO, HAO-RAN ZHENG, HONG ZHOU, and MAO-BIN HU. "EVOLUTIONARY GAMES IN MULTI-AGENT SYSTEMS OF WEIGHTED SOCIAL NETWORKS." International Journal of Modern Physics C 20, no. 05 (2009): 701–10. http://dx.doi.org/10.1142/s0129183109013923.

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Much empirical evidence has shown realistic networks are weighted. Compared with those on unweighted networks, the dynamics on weighted network often exhibit distinctly different phenomena. In this paper, we investigate the evolutionary game dynamics (prisoner's dilemma game and snowdrift game) on a weighted social network consisted of rational agents and focus on the evolution of cooperation in the system. Simulation results show that the cooperation level is strongly affected by the weighted nature of the network. Moreover, the variation of time series has also been investigated. Our work ma

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27

Stegagno, Paolo, and Chengzhi Yuan. "Distributed cooperative adaptive state estimation and system identification for multi-agent systems." IET Control Theory & Applications 13, no. 6 (2019): 815–22. http://dx.doi.org/10.1049/iet-cta.2018.6113.

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28

Yang, Zhaoxing, Haiming Jin, Rong Ding, et al. "DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10861–70. http://dx.doi.org/10.1609/aaai.v37i9.26288.

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In recent years, multi-agent reinforcement learning (MARL) has presented impressive performance in various applications. However, physical limitations, budget restrictions, and many other factors usually impose constraints on a multi-agent system (MAS), which cannot be handled by traditional MARL frameworks. Specifically, this paper focuses on constrained MASes where agents work cooperatively to maximize the expected team-average return under various constraints on expected team-average costs, and develops a constrained cooperative MARL framework, named DeCOM, for such MASes. In particular, De

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29

Song, Yang, and Jian Chen. "Agent-based MULTI-USV Intelligent Command and Control Cooperative System." Journal of Physics: Conference Series 1813, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1742-6596/1813/1/012035.

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30

Oliveira, EUGÉNIO, and Carlos Ramos. "A cooperative multi-agent system for an assembly robotic cell." Advanced Robotics 10, no. 1 (1995): 15–50. http://dx.doi.org/10.1163/156855396x00101.

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31

Adler, Jeffrey L., and Victor J. Blue. "A cooperative multi-agent transportation management and route guidance system." Transportation Research Part C: Emerging Technologies 10, no. 5-6 (2002): 433–54. http://dx.doi.org/10.1016/s0968-090x(02)00030-x.

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32

Huang, Keke, Xiaofang Chen, Zhaofei Yu, Chunhua Yang, and Weihua Gui. "Heterogeneous cooperative belief for social dilemma in multi-agent system." Applied Mathematics and Computation 320 (March 2018): 572–79. http://dx.doi.org/10.1016/j.amc.2017.10.018.

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33

Dittrich, Marc-André, and Silas Fohlmeister. "Cooperative multi-agent system for production control using reinforcement learning." CIRP Annals 69, no. 1 (2020): 389–92. http://dx.doi.org/10.1016/j.cirp.2020.04.005.

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34

Conte, Giuseppe, David Scaradozzi, Daniele Mannocchi, Paolo Raspa, Luca Panebianco, and Laura Screpanti. "Experimental testing of a cooperative ASV-ROV multi-agent system." IFAC-PapersOnLine 49, no. 23 (2016): 347–54. http://dx.doi.org/10.1016/j.ifacol.2016.10.428.

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35

Han, Wei, Bing Zhang, Qianyi Wang, Jun Luo, Weizhi Ran, and Yang Xu. "A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles." Applied Sciences 9, no. 6 (2019): 1089. http://dx.doi.org/10.3390/app9061089.

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The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent

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36

Feng, Zhi Jun, De Jian Zhou, Qi Fang Shen, and Yan Hui Chen. "Modeling of Network Cooperative Design of a Hydraulic System Based on Multi-Agent." Advanced Materials Research 317-319 (August 2011): 1424–28. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1424.

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To shorten the design period of a hydraulic system and achieve distributed cooperative design by experts, this paper proposes a model of the network collaborative design of a hydraulic system based on multi-agent technology. This model consists of three layers: the management agent layer, the middle agent layer and the resource agent layer. Its agent of the same layer adopts distributed structure, and each autonomous agent is constituted of a group of lower subordinate agents. This structure meets the need of distributed control of the cooperative design. By dynamic data exchange of agents, it

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37

Kadir, Herdawatie Abdul, and M. R. Arshad. "Cooperative Multi Agent System for Ocean Observation System based on Consensus Algorithm." Procedia Computer Science 76 (2015): 203–8. http://dx.doi.org/10.1016/j.procs.2015.12.343.

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38

Tominaga, Moeko, Yasunori Takemura, and Kazuo Ishii. "Modeling and Predicting Human Actions in Soccer Using Tensor-SOM." Applied Sciences 15, no. 9 (2025): 5088. https://doi.org/10.3390/app15095088.

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As robots become increasingly integrated into society, a future in which humans and robots collaborate is expected. In such a cooperative society, robots must possess the ability to predict human behavior. This study investigates a human–robot cooperation system using RoboCup soccer as a testbed, where a robot observes human actions, infers their intentions, and determines its own actions accordingly. Such problems have typically been addressed within the framework of multi-agent systems, where the entity performing an action is referred to as an ‘agent’, and multiple agents cooperate to compl

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39

CAO, JIAN, and SHENSHENG ZHANG. "AN INTEGRATED MULTI-AGENT CSCW SYSTEM FOR CONCURRENT PRODUCT DEVELOPMENT." International Journal of Information Technology & Decision Making 01, no. 03 (2002): 423–40. http://dx.doi.org/10.1142/s0219622002000270.

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Product development capability is more and more important for an enterprise in a knowledge-based economic era. In the philosophy of concurrent engineering, product development should be carried out in a concurrent way. Computer support is necessary for Concurrent Product Development (CPD). As an excellent tool to meet complex needs, CSCW has been used in CPD. But nearly all CSCW systems that have been developed so far concentrate on a more or less narrow sub-field of cooperative work. Thus, the need of integrated CSCW applications are apparent. The agent is a suitable programming paradigm that

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40

Kayser, Markus, Levi Cai, Sara Falcone, et al. "Design of a multi-agent, fiber composite digital fabrication system." Science Robotics 3, no. 22 (2018): eaau5630. http://dx.doi.org/10.1126/scirobotics.aau5630.

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Swarm-based fabrication of interwoven composite tubes via a fully autonomous, cooperative system can help create architectural-scale structures in effective and efficient ways, including in remote environments.

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41

Li, Yandong, Meichen Liu, Jiya Lian, and Yuan Guo. "Collaborative Optimal Formation Control for Heterogeneous Multi-Agent Systems." Entropy 24, no. 10 (2022): 1440. http://dx.doi.org/10.3390/e24101440.

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In this paper, the distributed optimal control method is used to study the cooperative formation of heterogeneous multi-agents in the air–ground environment. The considered system consists of an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). The optimal control theory is introduced into the formation control protocol, the distributed optimal formation control protocol is designed, and the stability is verified by graph theory. Furthermore, the cooperative optimal formation control protocol is designed, and the stability is analyzed using a block Kronecker product and matri

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42

Mo, Xiaoling, Daoyun Xu, and Zufeng Fu. "The Convergence of a Cooperation Markov Decision Process System." Entropy 22, no. 9 (2020): 955. http://dx.doi.org/10.3390/e22090955.

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In a general Markov decision progress system, only one agent’s learning evolution is considered. However, considering the learning evolution of a single agent in many problems has some limitations, more and more applications involve multi-agent. There are two types of cooperation, game environment among multi-agent. Therefore, this paper introduces a Cooperation Markov Decision Process (CMDP) system with two agents, which is suitable for the learning evolution of cooperative decision between two agents. It is further found that the value function in the CMDP system also converges in the end, a

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43

Han, Le, Weilong Song, Tingting Yang, Zeyu Tian, Xuewei Yu, and Xuyang An. "Cooperative Decisions of a Multi-Agent System for the Target-Pursuit Problem in Manned–Unmanned Environment." Electronics 12, no. 17 (2023): 3630. http://dx.doi.org/10.3390/electronics12173630.

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With the development of intelligent technology, multi-agent systems have been widely applied in military and civilian fields. Compared to a single platform, multi-agent systems can complete more dangerous, difficult, and heavy tasks. However, due to the limited autonomy of unmanned platforms and the regulatory needs of personnel, multi-agent systems cooperating with manned platforms to perform tasks have been more widely promoted at this stage of development. This paper addresses a differential game method for cooperative decision-making of a multi-agent system cooperating with the manned plat

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44

Liu, Xiaoli. "Research on decision-making strategy of soccer robot based on multi-agent reinforcement learning." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142091696. http://dx.doi.org/10.1177/1729881420916960.

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This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the action of other agents, so the reinforcement learning system needs to consider the joint state and joint action of multi-agent based on this. In addition, the application of this method in the cooperative strategy learning of soccer robot is studied, so that the multi-agent system can pass through the environment. To realize the division of labour and cooperation of multi-robots, the interactive learnin

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Ma, Jian Hong, Han Zhang, and Bao Feng He. "Applied-Information Technology with Mutil-Agent Cooperation in Education Model Control Engineering." Applied Mechanics and Materials 662 (October 2014): 255–58. http://dx.doi.org/10.4028/www.scientific.net/amm.662.255.

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To meet the demand of school's digitization, informationization, intelligent and practical,the design method of education model system based on Multi-agent is designed.We present the Multi-Agent cooperative processing model combining mobile Agent technology with the communication characteristics and modes of Multi-agent and the implementing technologies is researched in the education model.

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Wang, Wu Li, and Yan Jiang Wang. "Modeling of Multi-Agent System for Drilling Simulation." Advanced Materials Research 383-390 (November 2011): 1555–61. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1555.

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In view of the characteristics of the oil drilling process and the existing problems of traditional simulation system, a new distributed drilling simulation model was established based on Multi-Agent system (MAS) technology. By means of autonomous, cooperative and reactive characteristic of Agent, the drilling laws and phenomenon can be reflected promptly and accurately under any circumstances. The MAS modeling for oil drilling simulation, the structure and knowledge representation of each Agent and the communication among Agents are described in detail. Finally, an Agent-based normal drilling

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Tian, Lu, and Zhang Jianpeng. "Cooperative Solutions in Multi-Star Network Games." Contributions to Game Theory and Management 16 (2023): 132–44. http://dx.doi.org/10.21638/11701/spbu31.2023.09.

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A model also known as multi-agent systems, namely the multistar model is considered. In a multi-agent system, a two-level game is played, the first-level is the external game, and the second-level is the internal game. An approach is proposed how to distribute the benefits to players in the first-level game and the second-level game. The characteristic functions are constructed for the multi-star model. Based on the proposed characteristic functions, the combination of the Shapley value and the proportional solution as natural optimal principle to distribute the benefits in the first-level gam

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Ai, Ling, Shaozhen Tang, and Jie Yu. "Multi-agent cooperative encirclement based on improved MADDPG algorithm." Journal of Physics: Conference Series 2898, no. 1 (2024): 012033. http://dx.doi.org/10.1088/1742-6596/2898/1/012033.

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Abstract In this paper, we propose an improved Multi-agent Deep Deterministic Policy Gradient algorithm with a Priority Experience Replay mechanism (PER-MADDPG) to address the baseline algorithm’s high-dimensional state space challenges in multi-agent encirclement scenarios. The PER mechanism effectively mitigates the issue of non-stationary experience data distribution. By incorporating the Apollonian circle theory, we design an effective encirclement reward function that enables the multi-agent system to complete encirclement tasks in environments with static obstacles. Comparative simulatio

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Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (2011): 896–903. http://dx.doi.org/10.20965/jaciii.2011.p0896.

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward r

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He, Weizhen, Yuzhen Cheng, Pei Peng, Yuwen Yuan, and Shixuan Li. "Distributed Cooperative Formation Control for Multi-agent System based on Virtual Centre Strategy." Journal of Physics: Conference Series 2478, no. 10 (2023): 102026. http://dx.doi.org/10.1088/1742-6596/2478/10/102026.

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Abstract In this study, a distributed cooperative formation consistency control strategy is investigated for multi-agent system (MAS) to achieve formation consensus. Firstly, the virtual-centre-based reference formation is presented. Then a distributed cooperative formation control law based on virtual-centre is developed for MAS to achieve formation consensus. Finally, the new control scheme is applied to MAS consisting of 5 UAVs and the resulting closed-loop MAS is stable. The effectiveness of the new cooperative control behavior strategy is verified by simulations.

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