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Littérature scientifique sur le sujet « Multi-agent paradigms »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Multi-agent paradigms"

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Patel, Rakesh B., and Satish K. Shah. "Multi-agent controller using soft computational paradigms." Indian Journal of Applied Research 3, no. 2 (2011): 138–41. http://dx.doi.org/10.15373/2249555x/feb2013/48.

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HORLING, BRYAN, and VICTOR LESSER. "A survey of multi-agent organizational paradigms." Knowledge Engineering Review 19, no. 4 (2004): 281–316. http://dx.doi.org/10.1017/s0269888905000317.

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Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and

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Li, Mengxian, Qi Wang, and Yongjun Xu. "GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18368–76. https://doi.org/10.1609/aaai.v39i17.34021.

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The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number of agents increases, the inherent limitations of these frameworks significantly degrade the performance metrics, such as win rate, total reward, etc. To reduce the influence of the increasing number of agents on the performance metrics, we p

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Baykasoglu, Adil, Vahit Kaplanoglu, Rizvan Erol, and Cenk Sahin. "A MULTI-AGENT FRAMEWORK FOR LOAD CONSOLIDATION IN LOGISTICS." TRANSPORT 26, no. 3 (2011): 320–28. http://dx.doi.org/10.3846/16484142.2011.622141.

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Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and softwar

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Menassel, Yahia, Toufik Marir, and Farid Mokhati. "Operational profile development methodology for normative multi-agent systems." Multiagent and Grid Systems 19, no. 1 (2023): 1–22. http://dx.doi.org/10.3233/mgs-221507.

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Software reliability refers to the ability of a system to perform its intended function under specified conditions for a specified period of time. The first critical step in the software reliability testing process is to create a Software Operational Profile (SOP). Several methodologies for creating SOP have been proposed. Nonetheless, nearly all the proposed studies have neglected the uniqueness of the new software paradigms, despite the fact that these are generally distinguished by their own concepts and methodologies. One of these software paradigms is multi-agent systems. Rather than usin

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Mitra, Aritra. "Towards Robust Multi-Agent Reinforcement Learning." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 308. http://dx.doi.org/10.1609/aaaiss.v3i1.31222.

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Stochastic gradient descent (SGD) is at the heart of large-scale distributed machine learning paradigms such as federated learning (FL). In these applications, the task of training high-dimensional weight vectors is distributed among several workers that exchange information over networks of limited bandwidth. While parallelization at such an immense scale helps to reduce the computational burden, it creates several other challenges: delays, asynchrony, and most importantly, a significant communication bottleneck. The popularity and success of SGD can be attributed in no small part to the fact

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Li, Haiyang, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, and Donglin Zhu. "Multi-Agent Reinforcement Learning in Games: Research and Applications." Biomimetics 10, no. 6 (2025): 375. https://doi.org/10.3390/biomimetics10060375.

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Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks

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Surynek, Pavel. "Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12409–17. http://dx.doi.org/10.1609/aaai.v35i14.17472.

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We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We sug

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DARBYSHIRE, PAUL. "EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT." Advances in Complex Systems 06, no. 03 (2003): 405–26. http://dx.doi.org/10.1142/s0219525903000979.

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Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of lea

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Tokadlı, Güliz, Kaitlyn Ouverson, Chase Meusel, Austin Garcia, Stephen B. Gilbert, and Michael C. Dorneich. "An Analysis of Video Games Using the Dimensions of Human-Agent Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 716–20. http://dx.doi.org/10.1177/1541931218621163.

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Designers of human-agent interaction techniques may benefit from an analysis of existing video games that include aspects of human-agent teaming. Many popular multi-player video games have been designed to integrate multiple human and computer agents in pursuit of a common objective and can serve as a testbed to explore novel interaction methods in human-agent teams. A guiding framework of human-agent interaction was created to bridge best practices between video game and real-world domains. The framework was used to analyze games on five main dimensions: 1) Levels of Automation, 2) Levels of

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Thèses sur le sujet "Multi-agent paradigms"

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Moraïtis, Pavlos. "Paradigme multi-agent et prise de décision distribuée." Paris 9, 1994. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1994PA090046.

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Pawloski, Joel S. "Modeling tactical level combat using a Multi-agent System Design Paradigm (GI Agent)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA391678.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2001.<br>Thesis advisors, Michael Zyda, John E. Hiles. Includes bibliographical references (p. 81-82). Also available online.

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Furno, Domenico. "Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/927.

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2011 - 2012<br>The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to a

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Alqithami, Saad. "Network Organization Paradigm." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1293.

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In a complex adaptive system, diverse agents perform various actions without adherence to a predefined structure. The achievement of collaborative actions will be the result of continual interactions among them that shape a dynamic network. Agents may form an ad hoc organization based on the dynamic network of interactions for the purpose of achieving a long-term objective, which we termed a Network Organization (NO). Fervent and agile communication on social networking sites provides opportunities for potential issues to trigger individuals into individual actions as well as the attraction an

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Roddy, Kimberly A. Dickson Michael R. "Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://edocs.nps.edu/npspubs/scholarly/theses/2000/Sep/00Sep_Roddy.pdf.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, September 2000.<br>"September 2000." Thesis advisor(s): Michael Zyda, John Hiles. CD-ROM titled: RELATE: A relation-centric MAS simulation development package. Includes bibliographical references (p. 133-137). Also available online.

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Roddy, Kimberly A., and Michael R. Dickson. "Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm." Thesis, Monterey, California. Naval Postgraduate School, 2000. http://hdl.handle.net/10945/7739.

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Today's modeling and simulation communities are being challenged to create rich, detailed models incorporating human decision-making and organizational behavior. Recent advances in distributed artificial intelligence and complex systems theory have demonstrated that such ill-defined problems can be effectively modeled with agent-based simulation techniques using multiple, autonomoous, adaptive entities. RELATE, a relation-centric design paradigm for multi-agent systems (MAS), is presented to assist developers incorporate MAS solutions into their simulations. RELATe focuses the designer on six

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Dinc, Mustafa. "Modeling Bluetooth radio technology simulation using Multi-agent based system and Genetic Algorithm design paradigm." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA391713.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2001.<br>Thesis advisors, John E. Hiles, Michael Zyda. Includes bibliographical references (p. 107-109). Also Available online.

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Diot, Nicolas. "SAMP : Plateforme de modélisation à partir du paradigme multi-agents pour l’univers du jeu vidéo : vers un développement accessible et une gestion adaptée des interactions." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD065/document.

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En quelques années, les domaines des jeux vidéo et des systèmes multi-agents (SMA) ont pris de plus en plus de places dans la vie de chacun. Malgré des similitudes assez fortes (présences d’entité dans les vidéo pouvant être assimilées à des agents), les SMA ne sont presque jamais utilisés dans le développement de jeux. Ce mémoire présente Shine Agent Modeling Platform (SAMP), une plateforme visant intégrer le paradigme multi-agents au sein du développement de jeux vidéo. Cette fusion permet l’utilisation de la puissance des multi-agents au sein des jeux vidéo.SAMP propose une approche au nive

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Rasouli, Disfani Vahid. "Optimization and Control for Microgrid and Power Electronic Converters." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5764.

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The proposed dissertation research investigates Optimization and Control for Microgrid and Power Electronic Converters. The research has two major parts: i- Microgrid Operation and Control, ii- Power Electronic Converter Control and Optimization. In the first part, three focuses are investigated. First, a completely distributed algorithm is developed for dc optimal power flow problem for power distribution systems as one of the necessary functions considered in unit-commitment problem in day-ahead markets. This method is derived based upon the partial primal-dual representation of the economic

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Bevilacqua, Sara. "New biologically inspired models towards understanding the Italian Power Exchange market." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15036/.

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Questa tesi tratta l’estensione di un modello ad agenti per la simulazione e lo studio del mercato elettrico Italiano. Nel modello, gli agenti, che rappresentano le compagnie energetiche presenti nel mercato Italiano, competono tra loro con l’obiettivo di avere il più alto profitto. Ogni agente determina la propria strategia attraverso un algoritmo genetico, che opera su una popolazione di strategie. Questo modello presenta però due limitazioni: (1) agli impianti di produzione di un agente, situati nella stessa zona geografica e aventi in comune la tecnologia di produzione, viene applicata la

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Livres sur le sujet "Multi-agent paradigms"

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Roddy, Kimberly A. Modeling human and organizational behavior using a relation-centric multi-agent system design paradigm. Naval Postgraduate School, 2000.

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Perspective on Multi-Agent Systems: Paradigm, Formalism, Examples. Springer International Publishing AG, 2015.

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Modeling Tactical Level Combat Using a Multi-Agent System Design Paradigm (GI Agent). Storming Media, 2001.

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Modeling Bluetooth Radio Technology Simulation Using Multi-Agent Based System and Genetic Algorithm Design Paradigm. Storming Media, 2001.

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Chapitres de livres sur le sujet "Multi-agent paradigms"

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Tuyls, K., and P. Stone. "Multiagent Learning Paradigms." In Multi-Agent Systems and Agreement Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01713-2_1.

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Rybinski, Henryk, Dominik Ryżko, and Przemysław Więch. "Learning of Defaults by Agents in a Distributed Multi-Agent System Environment." In Emerging Paradigms in Machine Learning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28699-5_8.

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Poggi, Agostino, Giovanni Adorni, and Paola Turci. "Integrating Agent and Object-Oriented Programming Paradigms for Multi-Agent Systems Development." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-139.

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Mušić, Denis. "Mechanism for Adaptation of Group Decision-making in Multi-agent E-Learning Environment." In E-Learning Paradigms and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41965-2_9.

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Moise, Gabriela, Monica Vladoiu, and Zoran Constantinescu. "MASECO: A Multi-agent System for Evaluation and Classification of OERs and OCW Based on Quality Criteria." In E-Learning Paradigms and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41965-2_7.

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Kachaoui, Jabrane, and Abdessamad Belangour. "Improving a New Data Lake Architecture Design Based on Data Ponds and Multi-Agent Paradigms." In Innovations in Smart Cities Applications Volume 5. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94191-8_65.

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Amandi, Analía, Alejandro Zunino, and Ramiro Iturregui. "Multi-paradigm Languages Supporting Multi-agent Development." In Multi-Agent System Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48437-x_11.

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Louvieris, Panos, Andreas Gregoriades, Natasha Mashanovich, et al. "Agent-Based Parsimonious Decision Support Paradigm Employing Bayesian Belief Networks." In Defence Applications of Multi-Agent Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11683704_3.

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Chao, Sam, and Fai Wong. "A Multi-Agent Learning Paradigm for Medical Data Mining Diagnostic Workbench." In Data Mining and Multi-agent Integration. Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_12.

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Aguirre, Enrique, Arnulfo Alanis Garza, María del Rosario Baltazar, Lenin G. Lemus Zuñiga, Sergio Magdaleno Palencia, and Carlos Lino Ramírez. "Recognition of Primary Emotions Using the Paradigm of Intelligent Agents for the Recognition of Subtle Facial Expressions." In Agent and Multi-Agent Systems: Technologies and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19728-9_29.

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Actes de conférences sur le sujet "Multi-agent paradigms"

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Sharma, Neha, Monika Choudhary, Prakash Srivastava, Chander Prabha, and Himanshu Sharma. "A Review of Modern Computing Paradigms: Cloud, Fog, and Edge." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894412.

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Renney, Harri, Max Nethercott, Olivia Williams, Jayden Evetts, and James Lang. "Reimagining the Data Landscape: A Multi-Agent Paradigm for Data Interfacing." In 2025 8th International Conference on Data Science and Machine Learning Applications (CDMA). IEEE, 2025. https://doi.org/10.1109/cdma61895.2025.00025.

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Deepak, Ankur Dumka, and Bireshwar Dass Mazumdar. "Paradigm Shift in E-Commerce by Applying Cognitive Multi-Agent System with Machine Learning and Deep Learning Techniques." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829030.

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Tarale, Prithviraj, Edward Rietman, and Hava Siegelmann. "Distributed Multi-Agent Lifelong Learning." In 31st International Conference on Neural Information Processing. Tuwhera, 2025. https://doi.org/10.24135/iconip17.

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Lifelong learning (LL) systems must adapt to changing environments by continuously updating their knowledge. Traditional LL paradigms assume new data come labeled and that agents learn independently. However, labeled data are scarce, especially in remote settings. We introduce the Peer Parallel Lifelong Learning (PEEPLL) framework for distributed Multi-Agent LL, where agents actively seek peer assistance when needed. Unlike classical distributed AI, where communication scales poorly, PEEPLL agents reduce communication as their knowledge evolves. To improve resilience to low-quality peer respon

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Garfield, Keith, Annie Wu, Mehmet Onal, Britt Crawford, Adam Campbell, and Randall Shumaker. "The Effectiveness of Transferring Multi-Agent Behaviors From a Learning Environment in the Presence of Synthetic Social Structures." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81890.

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The diverse behavior representation schemes and learning paradigms being investigated within the robotics community share the common feature that successful deployment of agents requires that behaviors developed in a learning environment are successfully applied to a range of unfamiliar and potentially more complex operational environments. The intent of our research is to develop insight into the factors facilitating successful transfer of behaviors to the operational environments. We present experimental results investigating the effects of several factors for a simulated swarm of autonomous

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Picault, Sébastien, Yu-Lin Huang, Vianney Sicard, and Pauline Ezanno. "Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/53.

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The development of computational sciences has fostered major advances in life sciences, but also led to reproducibility and reliability issues, which become a crucial stake when simulations are aimed at assessing control measures, as in epidemiology. A broad use of software development methods is a useful remediation to reduce those problems, but preventive approaches, targeting not only implementation but also model design, are essential to sustainable enhancements. Among them, AI techniques, based on the separation between declarative and procedural concerns, and on knowledge engineering, of

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Lazarin, Nilson Mori, Carlos Eduardo Pantoja, and José Viterbo. "Towards a Toolkit for Teaching AI Supported by Robotic-agents: Proposal and First Impressions." In Workshop sobre Educação em Computação. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/wei.2023.229753.

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Using hardware platforms combined with other paradigms stimulates learning and has become an apparent trend in education. The Multi-Agent Systems domain studies the use of autonomous entities to solve complex problems. When combined with the Belief-Desire-Intention (BDI), a cognitive model close to human thinking, it can contribute to facilitating the teaching of Artificial Intelligence (AI) and Computational Thinking (CT). This work presents a toolkit to support the teaching of AI supported by BDI intelligent agents using a prototyping approach. Four workshops were held at different levels, a

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Sipele, Oscar, Victor Zamora, Agapito Ledezma, and Araceli Sanchis. "Advanced Driver's Alarms System through Multi-agent Paradigm." In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2018. http://dx.doi.org/10.1109/icite.2018.8492600.

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Song, Sung Keun, SeungWok Han, and Hee Yong Youn. "A New Agent Platform Architecture Supporting the Agent Group Paradigm for Multi-Agent Systems." In 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07). IEEE, 2007. http://dx.doi.org/10.1109/iat.2007.33.

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Fu, Haotian, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, and Changjie Fan. "Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/323.

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Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (or parameterized) action spaces which is very common in practice. Our work fills this gap by proposing two novel algorithms: Deep Multi-Agent Parameterized Q-Networks (Deep MAPQN) and Deep Multi-Agent Hierarchical Hybrid Q-Networks (Deep MAHHQN). We follow the centralized trainin

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