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Artículos de revistas sobre el tema "Multi-agent interaction"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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1

Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent sys
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2

Lorkiewicz,, Wojciech, and Radosław Katarzyniak. "Multi-participant Interaction in Multi-agent Naming Game." Computational Methods in Science and Technology 20, no. 2 (2014): 59–60. http://dx.doi.org/10.12921/cmst.2014.20.02.59-80.

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3

Penner, Robin R. "Multi-Agent Societies for Collaborative Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 15 (1996): 762–66. http://dx.doi.org/10.1177/154193129604001503.

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The application of a multi-agent architecture to the design and operation of automated process management systems is proving to be a fruitful method of facilitating human-system collaboration. The agent architecture we are developing is intended to be applied in environments where humans and automated systems jointly perform information intensive tasks, and is based on an organization of multiple agents, where both human and software agents are integrated members in groups akin to human societies. Important features of our architecture include an organization based on social structures, a user
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4

Boella, Guido, Joris Hulstijn, and Leendert van der Torre. "Interaction in Normative Multi-Agent Systems." Electronic Notes in Theoretical Computer Science 141, no. 5 (2005): 135–62. http://dx.doi.org/10.1016/j.entcs.2005.05.020.

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5

CHEREMISINOV, Dmitri. "The Specification of Agent Interaction in Multi-Agent Systems." Intelligent Information Management 01, no. 02 (2009): 65–72. http://dx.doi.org/10.4236/iim.2009.12011.

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6

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 – 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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7

Vogel-Heuser, Birgit, Matthias Seitz, Luis Alberto Cruz Salazar, Felix Gehlhoff, Alaettin Dogan, and Alexander Fay. "Multi-agent systems to enable Industry 4.0." at - Automatisierungstechnik 68, no. 6 (2020): 445–58. http://dx.doi.org/10.1515/auto-2020-0004.

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AbstractThe discussion paper “I4.0 language: vocabulary, message structure and semantic interaction protocols of the I4.0 language”, published by the working group “Semantics and Interaction of Industry 4.0 Components” of the GMA, also known as UAG of the AG 1 of the platform Industry 4.0 (I4.0), presented a concept for the language between I4.0 components. The main conclusion is: The increasing networking and cooperation of components enable new forms of organization and control. A clear understanding of machine interactions paves self-organized and self-optimized value creation in I4.0. Agen
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8

Sherstyugina, Anastasiya, and Roman Nesterov. "Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations." Proceedings of the Institute for System Programming of the RAS 35, no. 3 (2023): 11–32. http://dx.doi.org/10.15514/ispras-2023-35(3)-1.

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The structure of a process model directly discovered from an event log of a multi-agent system often does not reflect the behavior of individual agents and their interactions. We suggest analyzing the relations between events in an event log to localize actions executed by different agents and involved in their asynchronous interaction. Then, a process model of a multi-agent system is composed from individual agent models between which we add channels to model the asynchronous message exchange. We consider agent interaction within the acyclic and cyclic behavior of different agents. We develop
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9

Murakami, Yohei, Toru Ishida, Tomoyuki Kawasoe, and Reiko Hishiyama. "Multi-Agent Simulation Based on Interaction Design." Transactions of the Japanese Society for Artificial Intelligence 18 (2003): 278–85. http://dx.doi.org/10.1527/tjsai.18.278.

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10

Бредихин, А. В., Д. В. Веркошанский, Е. О. Неретин, and О. В. Собенина. "Intercomponent interaction in a multi-agent system." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 3(42) (2023): 22–23. http://dx.doi.org/10.26102/2310-6018/2023.42.3.022.

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Статья посвящена изучению механизмов межкомпонентного взаимодействия в мультиагентных системах. В работе рассмотрены различные подходы к обмену сообщениями между компонентами, а также преимущества и недостатки каждого из них. Определены ключевые проблемы межкомпонентного взаимодействия и предложены их решения. Особое внимание уделено механизму обмена сообщениями на основе брокера сообщений. В статье описаны принципы работы программного брокера, его преимущества и недостатки, а также примеры использования в мультиагентных системах. Результаты исследования показали, что использование брокера соо
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11

Poslad, Stefan. "Specifying protocols for multi-agent systems interaction." ACM Transactions on Autonomous and Adaptive Systems 2, no. 4 (2007): 15. http://dx.doi.org/10.1145/1293731.1293735.

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12

Zhou, Wenhong, Jie Li, Yiting Chen, and Lin-Cheng Shen. "Strategic Interaction Multi-Agent Deep Reinforcement Learning." IEEE Access 8 (2020): 119000–119009. http://dx.doi.org/10.1109/access.2020.3005734.

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13

Olaru, Andrei, and Monica Pricope. "Multi-Modal Decentralized Interaction in Multi-Entity Systems." Sensors 23, no. 6 (2023): 3139. http://dx.doi.org/10.3390/s23063139.

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Current multi-agent frameworks usually use centralized, fixed communication infrastructures for the entities that are deployed using them. This decreases the robustness of the system but is less challenging when having to deal with mobile agents that can migrate between nodes. We introduce, in the context of the FLASH-MAS (Fast and Lightweight Agent Shell) multi-entity deployment framework, methods to build decentralized interaction infrastructures which support migrating entities. We discuss the WS-Regions (WebSocket Regions) communication protocol, a proposal for interaction in deployments u
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14

Sharko, Polina A., Zhanna V. Burlutskaya, Daria A. Zubkova, Aleksei M. Gintciak, and Kapiton N. Pospelov. "AI-Supported Decision Making in Multi-Agent Production Systems Using the Example of the Oil and Gas Industry." Applied Sciences 15, no. 10 (2025): 5366. https://doi.org/10.3390/app15105366.

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This study focuses on the development of a decision support system for complex production systems. As a promising approach to resource allocation challenges, the application of AI tools, particularly the multi-agent approach, is proposed. It is hypothesized that a decision support system based on multi-agent systems (MASs), grounded in an invariant ontology and utilizing game-theoretic tools, will enhance the effectiveness of managerial decisions by accounting for the inherent multi-agent nature of complex production systems, manifested in diverse objective functions and intricate interaction
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15

Liu, Yong, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, and Yang Gao. "Multi-Agent Game Abstraction via Graph Attention Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7211–18. http://dx.doi.org/10.1609/aaai.v34i05.6211.

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In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment
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16

Sarraf Shirazi, Abbas, Sebastian von Mammen, and Christian Jacob. "Abstraction of agent interaction processes: Towards large-scale multi-agent models." SIMULATION 89, no. 4 (2013): 524–38. http://dx.doi.org/10.1177/0037549712470733.

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17

Liang, Hongtao, Fengju Kang, and Honghong Li. "UUV formation system modeling and simulation research based on Multi-Agent Interaction Chain." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 02 (2015): 1550019. http://dx.doi.org/10.1142/s1793962315500191.

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Unmanned Underwater Vehicle (UUV) formation system has an important role in the utilization of marine resource. In order to provide an efficient method to research modeling and simulation of UUV formation in the marine environment, the novel approach based on Multi-Agent Interaction Chain was proposed for the UUV formation system. Firstly, Multi-Agent Interaction Chain was analyzed, which mainly considered task and role of UUV in the formation, and the overall modeling process of UUV formation system based on Multi-Agent Interaction Chain was established. Then, the static structure of Multi-Ag
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18

Ko, Kwang-Eun, Jeong-Soo Lee, In-Hun Jang, and Kwee-Bo Sim. "Design of network for data interaction between Robot Agents in Multi Agent Robot System (MARS)." Journal of Korean Institute of Intelligent Systems 17, no. 5 (2007): 712–17. http://dx.doi.org/10.5391/jkiis.2007.17.5.712.

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19

HU, Jiang-Ping, Zhi-Xin LIU, Jin-Huan WANG, Lin WANG, and Xiao-Ming HU. "Estimation, Intervention and Interaction of Multi-agent Systems." Acta Automatica Sinica 39, no. 11 (2013): 1796. http://dx.doi.org/10.3724/sp.j.1004.2013.01796.

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20

Wang, Lin, Xiaofan Wang, and Xiaoming Hu. "Synchronization of multi-agent systems with topological interaction." IFAC Proceedings Volumes 44, no. 1 (2011): 14642–47. http://dx.doi.org/10.3182/20110828-6-it-1002.02244.

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21

KATAGAMI, Daisuke, Hidefumi OHMURA, Yoshiaki YASUMURA, and Katsumi NITTA. "10209 Multi User Learning Agent for Social Interaction." Proceedings of Conference of Kanto Branch 2005.11 (2005): 317–18. http://dx.doi.org/10.1299/jsmekanto.2005.11.317.

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22

HU, Jiang-Ping, Zhi-Xin LIU, Jin-Huan WANG, Lin WANG, and Xiao-Ming HU. "Estimation, Intervention and Interaction of Multi-agent Systems." Acta Automatica Sinica 39, no. 11 (2013): 1796–804. http://dx.doi.org/10.1016/s1874-1029(13)60078-6.

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23

Cantwell, John. "A Formal Model of Multi-Agent Belief-Interaction." Journal of Logic, Language and Information 14, no. 4 (2005): 397–422. http://dx.doi.org/10.1007/s10849-005-4019-8.

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24

Cantwell, John. "A Formal Model of Multi-Agent Belief-Interaction." Journal of Logic, Language and Information 15, no. 4 (2006): 303–29. http://dx.doi.org/10.1007/s10849-006-3776-3.

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25

Mariani, Stefano, and Andrea Omicini. "Special Issue “Multi-Agent Systems”: Editorial." Applied Sciences 10, no. 15 (2020): 5329. http://dx.doi.org/10.3390/app10155329.

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Multi-agent systems (MAS) are built around the central notions of agents, interaction, and environment. Agents are autonomous computational entities able to pro-actively pursue goals, and re-actively adapt to environment change. In doing so, they leverage on their social and situated capabilities: interacting with peers, and perceiving/acting on the environment. The relevance of MAS is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across many different application scenarios and business domains, ranging from logistics to soci
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26

Cliff, Oliver M., Joseph T. Lizier, X. Rosalind Wang, Peter Wang, Oliver Obst, and Mikhail Prokopenko. "Quantifying Long-Range Interactions and Coherent Structure in Multi-Agent Dynamics." Artificial Life 23, no. 1 (2017): 34–57. http://dx.doi.org/10.1162/artl_a_00221.

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We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team
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27

Benoudina, Lazhar, and Mohammed RedjimiRedjimi. "Multi Agent System Based Approach for Industrial Process Simulation." Journal Européen des Systèmes Automatisés​ 54, no. 2 (2021): 209–17. http://dx.doi.org/10.18280/jesa.540202.

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Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system in
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28

Kartvelishvili, V. M., and Е. A. Lebedyuk. "THE MODEL OF AGENT AND MULTI-AGENT INTERACTION IN SOCIO-ECONOMIC SYSTEMS." Vestnik of the Plekhanov Russian University of Economics, no. 3 (June 10, 2018): 147–65. http://dx.doi.org/10.21686/2413-2829-2018-3-147-165.

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29

LOW, CHI KEEN, RALPH RÖNNQUIST, and TSONG YUEH CHEN. "AN AUTOMATED TOOL (IDAF) TO MANIPULATE INTERACTION DIAGRAMS AND FRAGMENTATIONS FOR MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 09, no. 01 (1999): 127–49. http://dx.doi.org/10.1142/s0218194099000085.

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Interaction diagrams are used in multi-agent systems to graphically describe agent computation threads and communications while fragmentations are the algebraic representations of interaction diagrams. The IDAF (Interaction Diagrams And Fragmentations) tool suite has been developed based on the formalism of interaction diagrams and fragmentations. The tool suite consists of ValidatoR, FormatteR, TranslatoR, GrapheR and TesteR. This paper describes the usage of the tool suite and demonstrates it in two different multi-agent systems.
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30

ALONSO, EDUARDO, MARK D'INVERNO, DANIEL KUDENKO, MICHAEL LUCK, and JASON NOBLE. "Learning in multi-agent systems." Knowledge Engineering Review 16, no. 3 (2001): 277–84. http://dx.doi.org/10.1017/s0269888901000170.

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In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent
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31

Jha, Kunal, Tuan Anh Le, Chuanyang Jin, Yen-Ling Kuo, Joshua B. Tenenbaum, and Tianmin Shu. "Neural Amortized Inference for Nested Multi-Agent Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 530–37. http://dx.doi.org/10.1609/aaai.v38i1.27808.

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Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: lever
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32

Ali, A., M. Aslam, J. I. Janjua, and M. U. Chaudhry. "Methodology for Performance Evaluation of Distributed Multi Agent System." Nucleus 54, no. 2 (2017): 75–82. https://doi.org/10.71330/thenucleus.2017.61.

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This paper presents a methodology for the assessment of the distributed multi agent system. It requires apt attention to check as if multi agent system is performing well. In recent research performance is taken as a rate, queuing system or some measure without capturing all characterization of the multi agent system, there is a lag for the proper evaluation of multi agent system. Our methodology takes the generic characteristics of the distributed multi agent system which can affect the overall efficiency of the system, and spotted some performance metrics. These metrics depict the clear imag
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33

de Hauwere, Yann-Michaël, Sam Devlin, Daniel Kudenko, and Ann Nowé. "Context-sensitive reward shaping for sparse interaction multi-agent systems." Knowledge Engineering Review 31, no. 1 (2016): 59–76. http://dx.doi.org/10.1017/s0269888915000193.

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AbstractPotential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation.This paper describes the use of context-aware potential functions in a m
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34

Zhang, Yun, and Yan Lian Zhang. "Warehousing Management Based on Multi-Agent." Key Engineering Materials 467-469 (February 2011): 1598–603. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1598.

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This paper introduced Agent technology in the warehousing management in China, by taking advantage of its characteristics such as autonomy, reactivity, sociality and so on, and defining the interaction and cooperation mechanism between different Agent to achieve seamless connection between enterprises, so as to reduce or even to eliminate the inventory, supply viable idea and method of warehousing management for enterprises.
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35

Čavrak, Igor, Armin Stranjak, and Mario Žagar. "SDLMAS: A Scenario Modeling Framework for Multi-Agent Systems." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 898–925. https://doi.org/10.3217/jucs-015-04-0898.

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In this paper we analyze existing methods and languages for modeling agent interactions and propose a SDLMAS Framework for rapid design, development and runtime support of multi-agent systems. The framework provides a simple but expressive declarative language for modeling complex interactions among agents. Proposed language is based on scenarios, sequences of conversation actions directed towards achieving a goal. Scenario descriptions are converted into program code for a chosen target agent platform and system execution is supported by a runtime framework.
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36

Kim, Jonghoek. "Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction." Sensors 23, no. 19 (2023): 8050. http://dx.doi.org/10.3390/s23198050.

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This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown
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37

Telnov, Yu F., A. V. Danilov, R. I. Diveev, V. A. Kazakov, and E. V. Yaroshenko. "Development of a prototype of multi-agent system of network interaction of educational institutions." Open Education 22, no. 6 (2019): 14–26. http://dx.doi.org/10.21686/1818-4243-2018-6-14-26.

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The aim of the researchis to develop a prototype of the intelligent multi-agent system for dynamic interaction of the intelligent agents in the integrated information and educational space to solve the problem of formation of joint educational programs by several educational institutions.Materials and methods.In modern conditions of digital transformation of education the organization of network training of students on dynamically formed educational programs in accordance with the needs of the labor market and the individual requirements of students is becoming increasingly important. It is pr
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38

Hai-long, Liu, and Wu Tie-jun. "Analysis of the KQML model in multi-agent interaction." Journal of Zhejiang University-SCIENCE A 2, no. 2 (2001): 132–36. http://dx.doi.org/10.1631/jzus.2001.0132.

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39

KATAGAMI, Daisuke, Hidefumi OHMURA, Yoshiaki YASUMURA, and Katsumi NITTA. "Multi User Learning Agent (MULA) based on Social Interaction." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 17, no. 3 (2005): 340–50. http://dx.doi.org/10.3156/jsoft.17.340.

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40

Qu, Zhi Jian, Liang Guo, Hong Ping Ling, Ge Chen, and Li Liu. "Multi-Agent Information Interaction Research for Distribution Dispatch Monitoring." Advanced Materials Research 791-793 (September 2013): 962–66. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.962.

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In allusion to the transmission difficult difficulties problem of massive monitoring information flow, due to numerous on-line processing points and quick variation of operating parameters in distribution network monitoring dispatching, an new asynchronous processing method for batch information based on multi-agent alliance technology is proposed. Multi-agent alliance Platform is constructed by means of designing JACK software. Then using CIM-mapping technology and news event asynchronous trigger technology, massive data interactive real-time processing is implemented. Taking the monitoring s
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41

Schmitt, Gerhard, Maia Engeli, David Kurmann, Boi Faltings, and Stefan Monier. "Multi-agent interaction in a complex virtual design environment." AI Communications 9, no. 2 (1996): 74–78. http://dx.doi.org/10.3233/aic-1996-9206.

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42

Chen, Zhifu, Huimin Liao, and Tianguang Chu. "Clustering in multi-agent swarms via medium-range interaction." EPL (Europhysics Letters) 96, no. 4 (2011): 40015. http://dx.doi.org/10.1209/0295-5075/96/40015.

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43

Shevchenko, V. I., A. V. Skatkov, A. A. Bryukhovetskiy, O. V. Chengar, and T. A. Kokodey. "Multi-agent model of information interaction among unmanned vehicles." Journal of Physics: Conference Series 1515 (April 2020): 022039. http://dx.doi.org/10.1088/1742-6596/1515/2/022039.

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44

Sun, Yinshuang, Zhijian Ji, Qingyuan Qi, and Huizi Ma. "Bipartite Consensus of Multi-Agent Systems With Intermittent Interaction." IEEE Access 7 (2019): 130300–130311. http://dx.doi.org/10.1109/access.2019.2940541.

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45

Cao, Yang, Peiqing Li, Xiao Ling, and Qipeng Li. "FIF: future interaction forecasted for multi-agent trajectory prediction." Transportation Research Part C: Emerging Technologies 177 (August 2025): 105190. https://doi.org/10.1016/j.trc.2025.105190.

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46

Li, Tongyue, Dianxi Shi, Songchang Jin, Zhen Wang, Huanhuan Yang, and Yang Chen. "Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning." Entropy 27, no. 1 (2024): 4. https://doi.org/10.3390/e27010004.

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Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor–critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments. Specifically,
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47

Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.

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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the
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48

Cardoso, Rafael C., and Angelo Ferrando. "A Review of Agent-Based Programming for Multi-Agent Systems." Computers 10, no. 2 (2021): 16. http://dx.doi.org/10.3390/computers10020016.

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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent
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49

Cui, Zhoujuan, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, and Xiaoming Tao. "Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction." Journal of Physics: Conference Series 2833, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1742-6596/2833/1/012010.

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Abstract For intelligent transportation systems, accurately forecasting the future trajectories of multiple agents is pivotal. Considering the increased diversity of agents within a scene, in order to capture and model the variations in their appearance, motion status, behavioral patterns, and interrelationships, we propose a simple yet effective framework based on Spatio-Temporal-Interaction Graph Neural Networks. Specifically, a Multi-Class Agent Encoder is meticulously tailored to the specific class of each agent to distill pertinent information from their motion attributes and historical t
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Sun, Xuelong, Cheng Hu, Tian Liu, Shigang Yue, Jigen Peng, and Qinbing Fu. "Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics." Biomimetics 8, no. 8 (2023): 580. http://dx.doi.org/10.3390/biomimetics8080580.

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Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noi
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