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Artículos de revistas sobre el tema "Système de multi-agents"

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Publicado: 31 de agosto de 2024

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1

Sanders, Lena. "Les villes comme agents : simulation des futurs possibles du système urbain européen." Articles hors thème 5, no. 2 (2010): 153–80. http://dx.doi.org/10.7202/044081ar.

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Résumé L’approche systémique pour modéliser la dynamique des systèmes de villes est ancienne. Le concept d’auto-organisation et le formalisme des équations différentielles ont donné lieu à de nombreuses applications dans les années 1980. La simulation agent ouvre de nouvelles perspectives dans ce champ. L’objectif de cet article est d’abord de discuter des registres et du niveau d’explication qui sont mobilisés pour rendre compte des différentiels de croissance des villes. Il s’agit ensuite de montrer l’intérêt d’une approche agent pour formaliser des hypothèses au niveau méso-géographique des villes. Après un bref état de l’art sur le concept de systèmes de villes et les modèles spatio-temporels associés, le modèle EuroSim formalisé avec un système multi-agents est présenté. Celui-ci permet de simuler l’évolution des villes européennes entre 1950 et 2050 en testant différents scénarios relatifs à l’ouverture des frontières vers l’immigration non européenne et à l’existence ou non de barrières économiques internes.
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2

Enembreck, Fabrício, and Jean-Paul Barthès. "MAIS ­ Un système multi-agents pour la recherche d'information sur le web." Document numérique 8, no. 3 (2004): 83–106. http://dx.doi.org/10.3166/dn.8.3.83-106.

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3

Badariotti, Dominique, and Christiane Weber. "La mobilité résidentielle en ville. Modélisation par automates cellulaires et système multi-agents à Bogota." Espace géographique 31, no. 2 (2002): 97. http://dx.doi.org/10.3917/eg.312.0097.

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4

Duraffour, Françoise, Évelyne Mesclier, and Brice Anselme. "Comment freiner la propagation d’un virus respiratoire de type Sars-CoV-2 dans une périphérie urbaine défavorisée ? Réflexion à partir d’un système multi-agents spatialisé." L’Espace géographique Tome 51, no. 2 (2023): 139–58. http://dx.doi.org/10.3917/eg.512.0139.

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Il est difficile, voire impossible, de produire des données fiables sur la circulation d’un virus respiratoire comme le Sars-CoV-2 dans des contextes socio-spatiaux spécifiques. Le recours à un système multi-agents permet de simuler cette propagation dans des espaces de vie à partir de moments de proximité entre les agents et de tester des mesures de freinage, lesquelles, dans la réalité, impliquent des niveaux différents de participation des habitants et de la société civile. Les résultats permettent de comprendre des processus et de formuler des hypothèses sur les meilleures options en termes d’efficacité sanitaire. L’exemple, élaboré avec l’aide du collectif Norcovid , est celui d’un quartier périurbain défavorisé de Marseille.
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MOUTAOAKIL, Hind, and Icham JAMOULI. "Analyse et modélisation de la chaîne agrumicole marocaine basée sur le système multi-agents et indicateurs de performance : planification réactive et robuste." Revue Française de Gestion Industrielle 33, no. 4 (2014): 80–99. http://dx.doi.org/10.53102/2014.33.04.794.

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6

Chow, Justin, Umair Iftikhar, Victoria Weaver, Kwadwo Mponponsuo, and Amy Bromley. "Sudden Cardiac Death in a Patient with Systemic Lupus Erythematosus and Cytomegalovirus Myocarditis." Canadian Journal of General Internal Medicine 15, no. 4 (2020): 36–40. http://dx.doi.org/10.22374/cjgim.v15i4.400.

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BackgroundMyocarditis is generally a self-limited illness with a benign course. Certain pathogens—such as cytomegalovirus (CMV)—can cause severe/fulminant forms of myocarditis leading to congestive heart failure or sudden cardiac death (SCD). Case PresentationA 54-year-old female was diagnosed with systemic lupus erythematosus (SLE) in hospital and received inpatient immunosuppression due to significant multi-organ involvement. After a prolonged admission, she later died of an in-hospital cardiac arrest. Autopsy revealed CMV myocarditis involving the anterior wall of the left ventricle and cardiac conduction system. We postulated that both CMV and complications of SLE caused the patient’s SCD. DiscussionMyocarditis can vary in its presentation, severity, and diagnostic workup. Immunocompromised hosts are at risk of opportunistic infections such as CMV and are therefore prone to developing more severe forms of myocarditis. When caring for this patient population, it becomes necessary for clinicians to consider atypical manifestations of opportunistic pathogens in their diagnostic approach. RESUME Contexte La myocardite est généralement une maladie autolimitée dont l’évolution est bénigne. Certains agents pathogènes, tels que le cytomégalovirus (CMV), peuvent provoquer des formes graves/ fulminantes de myocardite entraînant une insuffisance cardiaque congestive ou une mort cardiaque subite (MCS). Présentation de casUne femme de 54 ans a reçu un diagnostic de lupus érythémateux disséminé (LED) à l’hôpital et a été hospitalisée pour une immunosuppression due à une importante atteinte de plusieurs organes. Après une longue hospitalisation, elle est décédée d’un arrêt cardiaque à l’hôpital. L’autopsie a révélé une myocardite à CMV impliquant la paroi antérieure du ventricule gauche et le système de conduction cardiaque. Nous avons émis l’hypothèse que le CMV et les complications du LED étaient tous deux à l’origine de l’arrêt cardiaque de la patiente. DiscussionLa myocardite peut varier dans sa présentation, sa gravité et son diagnostic. Les hôtes immunodéprimés sont exposés au risque d’infections opportunistes telles que le CMV et sont donc susceptibles de développer des formes plus graves de myocardite. Lorsqu’ils s’occupent de cette population de patients, les cliniciens doivent tenir compte des manifestations atypiques des agents pathogènes opportunistes dans leur approche diagnostique.
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7

Cointe, Nicolas, Grégory Bonnet, and Olivier Boissier. "Éthique collective dans les systèmes multi-agents." Revue d'intelligence artificielle 31, no. 1-2 (2017): 71–96. http://dx.doi.org/10.3166/ria.31.71-96.

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8

Balbo, Flavien, Emmanuel Adam, and René Mandiau. "Positionnement des systèmes multi-agents pour les systèmes de transport intelligents." Revue d'intelligence artificielle 30, no. 3 (2016): 299–327. http://dx.doi.org/10.3166/ria.30.299-327.

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9

Hutzler, Guillaume, Hanna Klaudel, and Dong Yue Wang. "Systèmes multi-agents adaptatifs avec contraintes temps-réel." Techniques et sciences informatiques 25, no. 3 (2006): 317–42. http://dx.doi.org/10.3166/tsi.25.317-342.

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10

Grislin-Le Strugeon, Emmanuelle. "Modélisation de l'adaptation organisationnelle dans les systèmes multi-agents." Revue d'intelligence artificielle 21, no. 5-6 (2007): 813–35. http://dx.doi.org/10.3166/ria.21.813-835.

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11

Scalas, Enrico. "Simulation informatisée en humanités et sciences sociales." Nouvelles perspectives en sciences sociales 5, no. 2 (2010): 59–67. http://dx.doi.org/10.7202/044075ar.

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RésuméCe texte court sur les simulations dans les sciences humaines et sociales est consacré aux simulations multi-agents. Les ingrédients d’une simulation multi-agents sont essentiellement au nombre de deux : agents et interactions. Les Agents avec leurs propriétés interagissent entre eux et les interactions peuvent être soit directes soit indirectes. Par analogie avec les systèmes physiques, la pertinence de l’équilibre statistique en économie peut être aussi analysée.
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12

Chang, Hervé, and Philippe Collet. "Négociation de contrats, des systèmes multi-agents aux composants logiciels." L'objet 12, no. 4 (2006): 73–102. http://dx.doi.org/10.3166/objet.12.4.73-102.

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13

-MONCLAR, François-Régis. "Rapide tour d'horizon des travaux sur les Systèmes Multi-Agents." Revue de l'Electricité et de l'Electronique -, no. 05 (2000): 89. http://dx.doi.org/10.3845/ree.2000.053.

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14

Breugnot, Paul, Bénédicte Herrmann, Christophe Lang, Laurent Philippe, and Alban Rousset. "Politiques de synchronisation dans les systèmes multi-agents distribués parallèles." Revue Ouverte d'Intelligence Artificielle 3, no. 5-6 (2022): 527–56. http://dx.doi.org/10.5802/roia.42.

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15

Cardon, Alain. "La complexité et le paradigme morphologique dans les systèmes multi-agents." Revue d'intelligence artificielle 21, no. 5-6 (2007): 601–16. http://dx.doi.org/10.3166/ria.21.601-616.

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16

Thomas, Vincent, Christine Bourjot, and Vincent Chevrier. "Construction de systèmes multi-agents par apprentissage collectif à base d'interactions." Revue d'intelligence artificielle 21, no. 5-6 (2007): 643–72. http://dx.doi.org/10.3166/ria.21.673-672.

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17

Crépin, Ludivine, Yves Demazeau, Olivier Boissier, and François Jacquenet. "Transaction de données sensibles au sein de systèmes multi-agents hippocratiques." Revue d'intelligence artificielle 23, no. 5-6 (2009): 621–50. http://dx.doi.org/10.3166/ria.23.621-650.

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18

Guivarch, Valérian, Valérie Camps, André Péninou, and Pierre Glize. "Apprentissage comportemental en continu de dispositifs ambiants par systèmes multi-agents adaptatifs." Revue d'intelligence artificielle 29, no. 1 (2015): 83–112. http://dx.doi.org/10.3166/ria.29.83-112.

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19

Di Bartolo, Florent. "Systèmes multi-agents et phénomènes d’émergence : donner forme par actions et interactions." Sciences du Design 4, no. 2 (2016): 63. http://dx.doi.org/10.3917/sdd.004.0063.

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20

Iguer, Hajar, Hicham Medromi, and Adil Sayouti. "The Impact of the 4th Wave on the Governance of Information Systems: IT Risk Architecture- EAS –SGRBased on Multi-Agents Systems." International Journal of Computer Theory and Engineering 6, no. 5 (2014): 432–37. http://dx.doi.org/10.7763/ijcte.2014.v6.904.

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21

Fadil, Hind, and Jean-Luc Koning. "Vers une approche formelle pour la validation des protocoles d'interaction en systèmes multi-agents." Ingénierie des systèmes d'information 13, no. 2 (2008): 33–52. http://dx.doi.org/10.3166/isi.13.2.33-52.

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22

MONTICOLO, Davy, Alex GABRIEL, and Pedro CHAVEZ BARRIOS. "Une approche de conception de systèmes multi-agents dédiés à la gestion des connaissances." Ingénierie des systèmes d'information 23, no. 2 (2018): 61–88. http://dx.doi.org/10.3166/isi.23.2.61-88.

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23

Doniec, Arnaud, René Mandiaud, Stéphane Espié, and Sylvain Piechowiak. "Comportements anticipatifs dans les systèmes multi-agents. Application à la simulation de trafic routier." Revue d'intelligence artificielle 21, no. 2 (2007): 183–221. http://dx.doi.org/10.3166/ria.21.183-221.

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24

Graja, Zeineb, Frédéric Migeon, Christine Maurel, Marie-Pierre Glezes, and Ahmed Hadj Kacem. "Vers une modélisation formelle basée sur le raffinement des systèmes multi-agents auto-organisateurs." Revue d'intelligence artificielle 30, no. 1-2 (2016): 159–83. http://dx.doi.org/10.3166/ria.30.159-183.

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25

SCHWEITZER, FRANK, and MATTHEW E. TAYLOR. "EDITORIAL: AGENTS AND MULTI-AGENT SYSTEMS." Advances in Complex Systems 14, no. 02 (2011): iii—iv. http://dx.doi.org/10.1142/s0219525911003025.

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26

Bonnefoy, Jean-Luc, François Bousquet, and Juliette Rouchier. "Modélisation d'une interaction individus, espace et société par les systèmes multi-agents : pâture en forêt virtuelle." Espace géographique 30, no. 1 (2001): 13. http://dx.doi.org/10.3917/eg.301.0013.

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27

Sanders, Lena, Denise Pumain, Hélène Mathian, F. Guérin-Page, and S. Bura. "Simulation de l'évolution du peuplement par les systèmes multi-agents (Simulation of a settlement system evolution by a multi-agent system)." Bulletin de l'Association de géographes français 74, no. 4 (1997): 385–96. http://dx.doi.org/10.3406/bagf.1997.1998.

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28

Beaufils, Bruno, Olivier Brandouy, Lin Ma, and Philippe Mathieu. "Simuler pour comprendre : un éclairage sur les dynamiques de marchés financiers à l'aide des systèmes multi-agents." Systèmes d'information & management 14, no. 4 (2009): 51. http://dx.doi.org/10.3917/sim.094.0051.

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29

Chari, Kaushal, and Manish Agrawal. "Multi-Issue Automated Negotiations Using Agents." INFORMS Journal on Computing 19, no. 4 (2007): 588–95. http://dx.doi.org/10.1287/ijoc.1060.0203.

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30

Zhang, Hao Lan, Clement H. C. Leung, and Gitesh K. Raikundalia. "Matrix-agent framework: A virtual platform for multi-agents." Journal of Systems Science and Systems Engineering 15, no. 4 (2006): 436–56. http://dx.doi.org/10.1007/s11518-006-5028-0.

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31

Michel, Fabien. "Le modèle IRM4S. De l'utilisation des notions d'influence et de réaction pour la simulation de systèmes multi-agents." Revue d'intelligence artificielle 21, no. 5-6 (2007): 757–79. http://dx.doi.org/10.3166/ria.21.757-779.

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32

Zhu, Haibin. "Group multi-role assignment with conflicting roles and agents." IEEE/CAA Journal of Automatica Sinica 7, no. 6 (2020): 1498–510. http://dx.doi.org/10.1109/jas.2020.1003354.

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33

Yang, Zhengquan, Qing Zhang, Zuolian Jiang, and Zengqiang Chen. "Flocking of multi-agents with time delay." International Journal of Systems Science 43, no. 11 (2012): 2125–34. http://dx.doi.org/10.1080/00207721.2011.564675.

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34

Fan, Dongyu, Haikuo Shen, and Lijing Dong. "Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking." Actuators 10, no. 10 (2021): 268. http://dx.doi.org/10.3390/act10100268.

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In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where N(N=3,N=5) agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking.
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35

Liu, Tengfei, and Zhong-Ping Jiang. "Distributed nonlinear control of mobile autonomous multi-agents." Automatica 50, no. 4 (2014): 1075–86. http://dx.doi.org/10.1016/j.automatica.2014.02.023.

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36

Mazumdar, Bireshwar Dass, and R. B. Mishra. "Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods." International Journal of Intelligent Information Technologies 6, no. 4 (2010): 46–70. http://dx.doi.org/10.4018/jiit.2010100104.

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The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.
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37

Yang, Min, Guanjun Liu, Ziyuan Zhou, and Jiacun Wang. "Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph Attention Network for UAV Swarms." Drones 7, no. 7 (2023): 476. http://dx.doi.org/10.3390/drones7070476.

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Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated significant advantages in some real-world scenarios, but the limited communication range of UAVs poses great challenges to multi-UAV collaborative decision-making. By constructing the multi-UAV cooperation problem as a multi-agent system (MAS), the cooperative decision-making among UAVs can be realized by using multi-agent reinforcement learning (MARL). Following this paradigm, this work focuses on developing partially observable MARL models that capture important information from local observations in order to select effective actions. Previous related studies employ either probability distributions or weighted mean field to update the average actions of neighborhood agents. However, they do not fully consider the feature information of surrounding neighbors, resulting in a local optimum often. In this paper, we propose a novel partially multi-agent reinforcement learning algorithm to remedy this flaw, which is based on graph attention network and partially observable mean field and is named as the GPMF algorithm for short. GPMF uses a graph attention module and a mean field module to describe how an agent is influenced by the actions of other agents at each time step. The graph attention module consists of a graph attention encoder and a differentiable attention mechanism, outputting a dynamic graph to represent the effectiveness of neighborhood agents against central agents. The mean field module approximates the effect of a neighborhood agent on a central agent as the average effect of effective neighborhood agents. Aiming at the typical task scenario of large-scale multi-UAV cooperative roundup, the proposed algorithm is evaluated based on the MAgent framework. Experimental results show that GPMF outperforms baselines including state-of-the-art partially observable mean field reinforcement learning algorithms, providing technical support for large-scale multi-UAV coordination and confrontation tasks in communication-constrained environments.
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38

Benford, Steve, John Bowers, Lennart E. Fahl�n, Chris Greenhalgh, and Dave Snowdon. "Embodiments, avatars, clones and agents for multi-user, multi-sensory virtual worlds." Multimedia Systems 5, no. 2 (1997): 93–104. http://dx.doi.org/10.1007/s005300050045.

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39

Bouzahzah, Mounira, and Ramdane Maamri. "An Approach for Fault Tolerance in Multi-Agent Systems using Learning Agents." International Journal of Intelligent Information Technologies 11, no. 3 (2015): 30–44. http://dx.doi.org/10.4018/ijiit.2015070103.

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Through this paper, the authors propose a new approach to get fault tolerant multi-agent systems using learning agents. Generally, the exceptions in the multi-agent system are divided into two main groups: private exceptions that are treated directly by the agents and global exceptions that combine all unexpected exceptions that need handlers to be solved. The proposed approach solves the problem of these global exceptions using learning agents. This work uses a formal model called hierarchical plans to model the activities of the system's agents in order to facilitate the exception detection and to model the communication with the learning agent. This latter uses a modified version of the Q Learning Algorithm in order to choose which handler can be used to solve an exceptions. The paper tries to give a new direction in the field of fault tolerance in multi-agent systems by using learning agents, the proposed solution makes it possible to adapt the handler used in case of failure within the context changes and treat repeated exceptions using learning agent experiences.
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40

Bouzouane, Abdenour. "Modélisation multi—agents pour une gestion stratégique d'une délégation d'assurances." Journal of Decision Systems 4, no. 1 (1995): 43–61. http://dx.doi.org/10.1080/12460125.1995.10511631.

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41

Javidi, M. Masoud, Hamid R. Naji, and M. E. Shiri. "Increasing the security of e-commerce systems using multi-agents." International Journal of Business Information Systems 4, no. 4 (2009): 460. http://dx.doi.org/10.1504/ijbis.2009.024506.

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42

Mercier, Annabelle, Clément Raievsky, Michel Occello, and Damien Genthial. "Solutions multi-agents pour la prise en charge à domicile des séniors." Ingénierie des systèmes d'information 18, no. 6 (2013): 83–112. http://dx.doi.org/10.3166/isi.18.6.83-112.

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43

Basingab, Mohammed. "Distributed Simulation with Multi-Agents for IoT in a Retail Pharmacy Facility." Information 11, no. 11 (2020): 527. http://dx.doi.org/10.3390/info11110527.

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Nowadays, internet of things (IoT) technology is considered as one of the key future technologies. The adoption of such technology is receiving quick attention from many industries as competitive pressures inspire them to move forward and invest. As technologies continue to advance, such as IoT, there is a vital need for an approach to identify its viability. This research proposes the adoption of IoT technology and the use of a simulation paradigm to capture the complexity of a system, offer reliable and continuous perceptions into its present and likely future state, and evaluate the economic feasibility of such adoption. A case study of one of the largest pharmacy retail chain is presented. IoT devices are suggested to be used to remotely monitor the failures of a geographically distributed system of refrigeration units. Multi-agents distributed system is proposed to simulate the operational behavior of the refrigerators and calculate the return of investment (ROI) of the proposed IoT implementation.
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44

Walczak, Steven. "Society of Agents." International Journal of Intelligent Information Technologies 14, no. 4 (2018): 1–23. http://dx.doi.org/10.4018/ijiit.2018100101.

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The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.
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45

Yang, Zhengquan, Xiaofang Pan, Qing Zhang, and Zengqiang Chen. "Formation control of first-order multi-agents with region constraint." Automatika 61, no. 4 (2020): 651–56. http://dx.doi.org/10.1080/00051144.2020.1814979.

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46

Kamide, Norihiro. "Inconsistency-Tolerant Multi-Agent Calculus." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 06 (2014): 815–29. http://dx.doi.org/10.1142/s0218488514500433.

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Verifying and specifying multi-agent systems in an appropriate inconsistency-tolerant logic are of growing importance in Computer Science since computer systems are generally used by or composed of inconsistency-tolerant multi-agents. In this paper, an inconsistency-tolerant logic for representing multi-agents is introduced as a Gentzen-type sequent calculus. This logic (or calculus) has multiple negation connectives that correspond to each agent, and these negation connectives have the property of paraconsistency that guarantees inconsistency-tolerance. The logic proposed is regarded as a modified generalization of trilattice logics, which are known to be useful for expressing fine-grained truth-values in computer networks. The completeness, cut-elimination and decidability theorems for the proposed logic (or sequent calculus) are proved as the main results of this paper.
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47

Weiskircher, Rene, Nectarios Kontoleon, Rodolfo Garcia-Flores, and Simon Dunstall. "Using agents for solving a multi-commodity-flow problem." European Journal of Operational Research 194, no. 3 (2009): 888–900. http://dx.doi.org/10.1016/j.ejor.2008.01.002.

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48

Wauters, T., K. Verbeeck, G. Vanden Berghe, and P. De Causmaecker. "Learning agents for the multi-mode project scheduling problem." Journal of the Operational Research Society 62, no. 2 (2011): 281–90. http://dx.doi.org/10.1057/jors.2010.101.

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49

Freire, Ismael T., Xerxes D. Arsiwalla, Jordi-Ysard Puigbò, and Paul Verschure. "Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control." Information 14, no. 8 (2023): 441. http://dx.doi.org/10.3390/info14080441.

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A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the computational principles to model a Theory of Mind (ToM)? Deep learning approaches to address these questions fall short of a better understanding of the problem. In part, this is due to the black-box nature of deep networks, wherein computational mechanisms of ToM are not readily revealed. Here, we consider alternative hypotheses seeking to model how the brain might realize a ToM. In particular, we propose embodied and situated agent models based on distributed adaptive control theory to predict the actions of other agents in five different game-theoretic tasks (Harmony Game, Hawk-Dove, Stag Hunt, Prisoner’s Dilemma, and Battle of the Exes). Our multi-layer control models implement top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We test cooperative and competitive strategies among seven different agent models (cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive, and internal agents). We show that, compared to pure reinforcement-based strategies, probabilistic learning agents modeled on rational, predictive, and internal phenotypes perform better in game-theoretic metrics across tasks. The outlined autonomous multi-agent models might capture systems-level processes underlying a ToM and suggest architectural principles of ToM from a control-theoretic perspective.
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50

Chen, Zhiyong, and Hai-Tao Zhang. "A remark on collective circular motion of heterogeneous multi-agents." Automatica 49, no. 5 (2013): 1236–41. http://dx.doi.org/10.1016/j.automatica.2013.01.017.

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