Relevant bibliographies by topics / Multi-agent interaction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi-agent interaction'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multi-agent interaction"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Multi-agent interaction"

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Chen, Xudong. "Multi-Agent Systems with Reciprocal Interaction Laws." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11424.

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In this thesis, we investigate a special class of multi-agent systems, which we call reciprocal multi-agent (RMA) systems. The evolution of agents in a RMA system is governed by interactions between pairs of agents. Each interaction is reciprocal, and the magnitude of attraction/repulsion depends only on distances between agents. We investigate the class of RMA systems from four perspectives, these are two basic properties of the dynamical system, one formula for computing the Morse indices/co-indices of critical formations, and one formation control model as a variation of the class of RMA
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Kalenka, Susanne. "Modelling social interaction attitudes in multi-agent systems." Thesis, Queen Mary, University of London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395937.

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Perez, Jorge (Jorge I. ). "Designing interaction for human-machine collaboration in multi-agent scheduling." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/106007.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 57-58).<br>In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms
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Bai, Xi. "Peer-to-peer, multi-agent interaction adapted to a web architecture." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7968.

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The Internet and Web have brought in a new era of information sharing and opened up countless opportunities for people to rethink and redefine communication. With the development of network-related technologies, a Client/Server architecture has become dominant in the application layer of the Internet. Nowadays network nodes are behind firewalls and Network Address Translations, and the centralised design of the Client/Server architecture limits communication between users on the client side. Achieving the conflicting goals of data privacy and data openness is difficult and in many cases the di
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Anacleto, Louçã Jorge. "Cartographie cognitive, réflexion stratégique et interaction distribuée : une approche multi-agent." Paris 9, 2000. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2000PA090028.

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Dans cette thèse nous adoptons une perspective interdisciplinaire concernant les domaines de la prise de décision stratégique dans les organisations humaines, de la cartographie cognitive et des systèmes multi-agents. Nous proposons la modélisation du raisonnement multidimensionnel caractéristique des processus stratégiques à travers un système multi-agent. Pour cela, nous utilisons la cartographie cognitive en tant qu'instrument d'aide à la réflexion collective. Dans le système multi-agent TOMAS, des cartes cognitives sont utilisées pour représenter les connaissances d'agents artificiels, cog
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Dinu, Razvan. "Web Agents : towards online hybrid multi-agent systems." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20126/document.

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Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have th
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Scown, Philip J. A. "Knowledge needs analysis for simultaneously multi-agent real-time systems." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/26859.

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A set of systems are considered where there are multiple agents simultaneously active within real-time a environment. These systems are termed SMART systems and are found in domains as diverse as office administration, process control and aviation. Such systems place particular demands on agents that are not present in non-SMART systems. Actions may be time constrained in two ways: i) an action appropriate at one time may not be appropriate at another; ii) the time available for a required action may approximate to the time in which an agent is able to perform that action. In order to be able
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GRALHOZ, RICARDO AUGUSTO RODRIGUES. "LAWML: A LANGUAGE FOR MODELING INTERACTION LAWS IN OPEN MULTI-AGENT SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11626@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>O paradigma de agentes surgiu visando atender à necessidade de novas abstrações para o desenvolvimento de sistemas complexos e distribuídos. Para lidar com a mprevisibilidade do comportamento dos sistemas multi-agentes abertos, que são sistemas concorrentes e assíncronos formados por diversos agentes que agem com certo grau de autonomia e que podem interagir entre si para alcançar objetivos individuais, são usados mecanismos de governança na regulação das interações. Na maioria das abordagens existentes, a especificação da
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Cunningham, Bryan. "Non-Reciprocating Sharing Methods in Cooperative Q-Learning Environments." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/34610.

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Past research on multi-agent simulation with cooperative reinforcement learning (RL) for homogeneous agents focuses on developing sharing strategies that are adopted and used by all agents in the environment. These sharing strategies are considered to be reciprocating because all participating agents have a predefined agreement regarding what type of information is shared, when it is shared, and how the participating agent's policies are subsequently updated. The sharing strategies are specifically designed around manipulating this shared information to improve learning performance. This thesi
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Salge, Christoph. "Information theoretic models of social interaction." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/13887.

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This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of 'maximisation of relevant information' and 'information parsimony' can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this differentiates other agents from the rest of the environ
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Books on the topic "Multi-agent interaction"

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1960-, Sun Ron, ed. Cognition and multi-agent interaction: From cognitive modeling to social simulation. Cambridge University Press, 2006.

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Khosla, Rajiv. Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach. Springer US, 2000.

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Iyad, Rahwan, Parsons Simon, and SpringerLink (Online service), eds. Argumentation in Multi-Agent Systems: 7th International Workshop, ArgMAS 2010 Toronto, ON, Canada, May 10, 2010 Revised, Selected and Invited Papers. Springer Berlin Heidelberg, 2011.

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International Conference on Multi-Agent Systems (1st 1995 San Francisco, Calif.). ICMAS--95, First International Conference on Multi-Agent Systems: Proceedings, June 12-14, 1995, San Francisco, California. AAAI Press, 1995.

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Sun, Ron, ed. Cognition and Multi-Agent Interaction. Cambridge University Press, 2005. http://dx.doi.org/10.1017/cbo9780511610721.

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W, Brockett Roger, Tarokh Vahid, and Lu Yue, eds. Multi-Agent Systems with Reciprocal Interaction Laws. 2014.

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Sun, Ron. Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2008.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2005.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2009.

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Book chapters on the topic "Multi-agent interaction"

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Chen, Chung-Chi, and Hiroya Takamura. "Multi-agent Interaction." In SpringerBriefs in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94687-5_4.

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Abstract Teamwork, or working collaboratively, has the potential to increase productivity, foster innovation, enhance problem-solving abilities, and improve skills among members. It is commonly practiced in professional settings. With the development of AI agents, there is an opportunity to enable agents to discuss with each other using natural language.
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Torii, Daisuke, Toru Ishida, Stéphane Bonneaud, and Alexis Drogoul. "Layering Social Interaction Scenarios on Environmental Simulation." In Multi-Agent and Multi-Agent-Based Simulation. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32243-6_7.

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Chen, Hongbing, Qun Yang, and Manwu Xu. "A Calculus for MAS Interaction Protocol." In Agent Computing and Multi-Agent Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802372_6.

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Klügl, Franziska. "Affordance-Based Interaction Design for Agent-Based Simulation Models." In Multi-Agent Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17130-2_4.

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Michel, Fabien, Abdelkader Gouaïch, and Jacques Ferber. "Weak Interaction and Strong Interaction in Agent Based Simulations." In Multi-Agent-Based Simulation III. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24613-8_4.

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El Fallah-Seghrouchni, Amal, Serge Haddad, and Hamza Mazouzi. "Protocol Engineering for Multi-agent Interaction." In Multi-Agent System Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48437-x_8.

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Tanaka, Rie, Hideyuki Nakanishi, and Toru Ishida. "Coordination of Concurrent Scenarios in Multi-agent Interaction." In Agent Computing and Multi-Agent Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802372_29.

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Lin, Aizhong. "Strategic Multi-Personal-Agent Interaction." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44637-0_7.

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Tagiew, Rustam. "Multi-Agent-System for General Strategic Interaction." In Agent and Multi-Agent Systems: Technologies and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01665-3_65.

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Zatelli, Maicon Rafael, and Jomi Fred Hübner. "The Interaction as an Integration Component for the JaCaMo Platform." In Engineering Multi-Agent Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14484-9_22.

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Conference papers on the topic "Multi-agent interaction"

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Hsu, Christopher D., Mulugeta A. Haile, and Pratik Chaudhari. "A Model for Multi-Agent Heterogeneous Interaction Problems." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644600.

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Menti, Giridhar Babu, V. Ravindranadh Chowdary, Naga Lakshmi Pujitha Nagasarapu, Guru Kavya Godugunuri, Pramod Abburi, and Jaswanth Yerramsetty. "Semi-Autonomous Teleoperated Robot for Enhanced Human-Machine Interaction." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894321.

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Mazumdar, Bireshwar Dass, Prashant K. Gupta, and Rama S. Komaragiri. "Multi-Agent Based Social Interaction Model for Neurological Disorder." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10695958.

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Woo, Sungmin, Minjung Kim, Donghyeong Kim, Sungjun Jang, and Sangyoun Lee. "FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611080.

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Sun, Lingfeng, Pin-Yun Hung, Changhao Wang, Masayoshi Tomizuka, and Zhuo Xu. "Distributed Multi-Agent Interaction Generation with Imagined Potential Games." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644523.

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Chen, Libin, Luyao Wang, Qian Li, Yuqun Wang, and Hongfu Liu. "AdvCommNet: Enhancing Multi-Agent Interaction in Deep Reinforcement Learning Systems." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661908.

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Yu, Ying, Shili Luo, Tingwen Chen, Yanru He, and Jiahui Wu. "A multi-agent system for indirect reciprocity with optional interaction." In Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), edited by Xiangjie Kong and Xingjian Wang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049230.

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Shen, Xinyu. "Enhancing Multi-Agent Reinforcement Learning via Entity Aware-Interaction Network." In 2024 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, 2024. https://doi.org/10.1109/ichci63580.2024.10808016.

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Yue, Shengbin, Ting Huang, Zheng Jia, et al. "Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.365.

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Yang, Kaiyuan, Chao Ming, Suyu Yan, and Zhihua Wei. "Cooperative guidance control design for multi-agent vehicles." In Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), edited by Xiangjie Kong and Xingjian Wang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049286.

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Reports on the topic "Multi-agent interaction"

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Spears, William, Diana Spears, Wesley Kerr, Suranga Hettiarachchi, and Dimitri Zarzhitsky. Optimizing Interaction Potentials for Multi-Agent Surveillance. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada434929.

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Roszman, Larry, Derek Armstrong, Aram Khalali, and Gwen Hickling. Dynamic Control and Formal Models of Multi-Agent Interactions and Behaviors. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada435125.

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Coble, Jeff, Larry Roszman, and Tiffany Frazier. Dynamic Control and Formal Models of Multi-Agent Interactions and Behaviors. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada412536.

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Pasupuleti, Murali Krishna. AI in Global Strategy: Harnessing Game Theory and Reinforcement Learning for Diplomatic Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rr125.

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Abstract: This article explores the integration of game theory and reinforcement learning (RL) in the context of global diplomacy, emphasizing the transformative potential of strategic AI in international relations. It provides an in-depth analysis of how game theory principles, such as Nash equilibrium and cooperative strategies, are leveraged by AI to model and optimize diplomatic interactions. The Article explains how reinforcement learning enables AI systems to learn and adapt strategies over time, improving their effectiveness in negotiation and conflict resolution. Through case studies a
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Manulis, Shulamit, Christine D. Smart, Isaac Barash, Guido Sessa, and Harvey C. Hoch. Molecular Interactions of Clavibacter michiganensis subsp. michiganensis with Tomato. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7697113.bard.

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Clavibacter michiganensis subsp. michiganensis (Cmm), the causal agent of bacterial wilt and canker of tomato, is the most destructive bacterial disease of tomato causing substantial economic losses in Israel, the U.S.A. and worldwide. The molecular strategies that allow Cmm, a Gram-positive bacterium, to develop a successful infection in tomato plants are largely unknown. The goal of the project was to elucidate the molecular interactions between Cmmand tomato. The first objective was to analyze gene expression profiles of susceptible tomato plants infected with pathogenic and endophytic Cmms
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Ficht, Thomas, Gary Splitter, Menachem Banai, and Menachem Davidson. Characterization of B. Melinensis REV 1 Attenuated Mutants. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7580667.bard.

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Brucella Mutagenesis (TAMU) The working hypothesis for this study was that survival of Brucella vaccines was directly related to their persistence in the host. This premise is based on previously published work detailing the survival of the currently employed vaccine strains S19 and Rev 1. The approach employed signature-tagged mutagenesis to construct mutants interrupted in individual genes, and the mouse model to identify mutants with attenuated virulence/survival. Intracellular survival in macrophages is the key to both reproductive disease in ruminants and reticuloendothelial disease obser
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