Bibliografías temáticas / Interaction multi-agents

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Literatura académica sobre el tema "Interaction multi-agents"

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

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

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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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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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Bucher, Andreas, Mateusz Dolata, Sven Eckhardt, Dario Staehelin, and Gerhard Schwabe. "Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions." Proceedings of the ACM on Human-Computer Interaction 8, GROUP (2024): 1–25. http://dx.doi.org/10.1145/3633072.

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Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations f
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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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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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Emelyanov, Viktor V. "Organization of the Agents Interaction in Multi-Agents of Production Coordination System." IFAC Proceedings Volumes 33, no. 17 (2000): 485–89. http://dx.doi.org/10.1016/s1474-6670(17)39450-8.

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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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ZHANG, Kun, Yoichiro MAEDA, and Yasutake TAKAHASHI. "Learning Model Considering the Interaction among Heterogeneous Multi-Agents." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 24, no. 5 (2012): 1002–11. http://dx.doi.org/10.3156/jsoft.24.1002.

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

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward r
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Jin, Kun, Yevgeniy Vorobeychik, and Mingyan Liu. "Multi-Scale Games: Representing and Solving Games on Networks with Group Structure." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5497–505. http://dx.doi.org/10.1609/aaai.v35i6.16692.

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Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage
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Tesis sobre el tema "Interaction multi-agents"

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Kumar, Rohit. "Socially Capable Conversational Agents for Multi-Party Interactive Situations." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/162.

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Since the inception of AI research, great strides have been made towards achieving the goal of extending natural language conversation as a medium of interaction with machines. Today, we find many Conversational Agents (CAs) situated in various aspects of our everyday life such as information access, education and entertainment. However, most of the existing work on CAs has focused on agents that support only one user in each interactive session. On the other hand, people organize themselves in groups such as teams of co-workers, family and networks of friends. With the mass-adoption of Intern
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2

Riberio, Alexandre Moretto. "Un modèle d'interaction dynamique pour les systèmes multi-agents." Université Joseph Fourier (Grenoble), 2000. http://www.theses.fr/2000GRE10035.

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Cette thèse traite le problème de l'interaction dans les Systèmes Multi-Agents. L'approche choisie consiste à analyser l'interaction comme un composant à part entière dans la conception d'un système. Nous présentons un modèle d'interaction où le mécanisme d'interaction joue un rôle dynamique, c'est-à-dire qui une partie de la gestion de l'interaction est déplacée de l'agent vers le mécanisme d'interaction. Le transfert se fait sous la forme d'une délégation des tâches, concernant l'interaction. La répartition de l'interaction permet le développement des interactions indépendamment des agents,
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Pin, Paolo <1974&gt. "Four multi-agents economic models: from evolutionary competition to social interaction." Doctoral thesis, Università Ca' Foscari Venezia, 2007. http://hdl.handle.net/10579/396.

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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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5

Pauchet, Alexandre. "Modélisation cognitive d'interactions humaines dans un cadre de planification multi-agents." Paris 13, 2006. http://www.theses.fr/2006PA132017.

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Cette thèse s'appuie sur une expérimentation psychologique ayant permis de concevoir des modèles cognitifs de la planification humaine et de l'interaction humaine. Ces deux modèles sont intégrés de façon homogène à une nouvelle architecture d'agent appelée BDIggy, née d'une fusion entre le système Iggy (un système pré-existant de planification humaine) et une architecture BDI étendue à la résolution coope��rative de deux problèmes. Le modèle de l'interaction humaine introduit dans BDIggy est sur deux niveaux : un modèle de l'énoncé et un modèle du discours liés par une sémantique des performat
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Ouadou, Kamel Eddine. "Amf : Un modèle d'architecture multi-Agents Multi-Facettes pour interfaces homme-machine et les outils associés." Ecully, Ecole centrale de Lyon, 1994. http://www.theses.fr/1994ECDL0038.

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Un modele d'architecture pour interfaces homme-machine propose une structuration du logiciel interactif (li) avec comme objectif de faciliter l'elaboration de l'interface homme-machine de qualite, d'automatiser cette elaboration et prendre naturellement en compte des standards. Plusieurs modeles ont ete proposes, mais aucun d'entre eux n'est encore arrive a satisfaire l'ensemble des objectifs identifies. Nous avons etudie, dans le cadre de cette these, les principaux aspects qui interviennent dans l'elaboration d'un logiciel interactif et qui conditionnent son architecture. (des styles et mode
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Marzougui, Borhen. "Contribution à la modélisation et à la vérification des systèmes multi agents." Thesis, Paris, CNAM, 2014. http://www.theses.fr/2014CNAM0918/document.

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Les Réseaux de Petri (RdP) sont actuellement les approches les plus prometteuses pour modéliser et vérifier les systèmes complexes tels que les Systèmes Multi Agents (SMA). De nombreuses solutions ont été proposées pour remédier aux problèmes de communication, de coordination et d’interaction entre les Agents. Cependant, il n’existe aucune en mesure de traiter, à la fois les aspects structurels et comportementaux, du moins à notre connaissance. La thèse s'intéresse à la problématique de modélisation formelle et de vérification automatique et semi-automatique de propriétés pour les Systèmes Mul
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CARVALHO, GUSTAVO ROBICHEZ DE. "G-FRAMEWORKS: AN APPROACH TO PROMOTE THE REUSE OF INTERACTION LAWS IN OPEN MULTI-AGENTS SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10169@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Um dos desafios de desenvolvimento de software é produzir aplicativos que são projetados para evoluir reduzindo esforços de manutenção. Diversas técnicas desenvolvidas para a governança de leis de interação em sistemas multiagentes abertos foram propostas, no entanto a flexibilidade e a reutilização de leis não ocorrem de forma sistemática com estas técnicas. A tecnologia de gframeworks visa orientar o projeto e a implementação de leis de interação em sistemas multiagent
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Goracci, Laura. "Interaction of surfactants with DNA : a multi-technical approach to the study of DNA transfection agents." Bordeaux 1, 2004. http://www.theses.fr/2004BOR12783.

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Les interactions entre l'ADN et une série de molécules amphiphiles ont étés étudiés en combinant des techniques spectroscopiques anisotropes (absorption fluorescence) et polarisées (dichroi͏̈sme circulaire, dépolarisation de fluorescence). Les résultats obtenus ont été ensuite analysés en utilisant un modele de docking moléculaire et indiquent que l'utilisation de la sonde fluorescente bromure d'éthidium pour étudier les interactions entre ADN et amphiphiles est bien plus complexe que ce postulé actuellement dans la littérature. L'utilisation d'une nouvelle sonde, le composé Hoechst 33258, est
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Persson, Christian. "Strategies for enhancing consumer interaction in electronic retailing." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3267.

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Libros sobre el tema "Interaction multi-agents"

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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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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, 2009.

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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, 2006.

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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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Smart, Paul R. Mandevillian Intelligence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198801764.003.0013.

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations, and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. As a specific form of collective intel
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Barsoum, Rashad S. Schistosomiasis. Edited by Neil Sheerin. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0182_update_001.

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AbstractSchistosomiasis is a parasitic disease that affects millions of people in 78 countries, where it is held responsible for considerable morbidity and mortality. It is caused by a blood fluke, which provokes an immunological response to hundreds of its antigens. This induces multi-organ pathology through the formation of tissue granulomata or circulating immune complexes. In addition, it is amyloidogenic and carcinogenic, through the interaction of immunological perturbation with confounding metabolic and genetic factors. The primary targets of schistosomiasis are urinary and hepatointest
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Capítulos de libros sobre el tema "Interaction multi-agents"

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Hassan, Mohd Fadzil, and Dave Robertson. "Addressing the Brittleness of Agent Interaction." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_24.

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McGinnis, Jarred, and David Robertson. "Dynamic and Distributed Interaction Protocols." In Adaptive Agents and Multi-Agent Systems II. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32274-0_11.

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Oluyomi, Ayodele, and Leon Sterling. "A Dedicated Approach for Developing Agent Interaction Protocols." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32128-6_13.

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Ribeiro, Richardson, Douglas M. Guisi, Marcelo Teixeira, Eden R. Dosciatti, Andre P. Borges, and Fabrício Enembreck. "Combination of Interaction Models for Multi-Agents Systems." In Enterprise Information Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62386-3_5.

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Chen, Chung-Chi, and Hiroya Takamura. "Multi-scale Model Synergy." In SpringerBriefs in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94687-5_5.

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Abstract In the previous chapters, we discussed the design of AI agents and models, as well as the potential of employing AI agents for various scales of collaboration and interaction. As mentioned in Sect. 1.3, AI models should not be dismissed in the LLM era; instead, consideration should be given to achieving synergy through multi-scale model interaction. To this end, this chapter begins with the traditional concept of data augmentation applied to novel tasks within financial scenarios in Sect. 5.1, followed by a discussion on the dynamic interaction loop between large and small language mo
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Barbuceanu, Mihai, and Wai-Kau Lo. "Integrating Conversational Interaction and Constraint Based Reasoning in an Agent Building Shell." In Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1_13.

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Bakar, Najwa Abu, and Ali Selamat. "Assessing Agents Interaction Quality via Multi-agent Runtime Verification." In Computational Collective Intelligence. Technologies and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40495-5_18.

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Soic, Renato, Pavle Skocir, and Gordan Jezic. "Agent-Based System for Context-Aware Human-Computer Interaction." In Agents and Multi-Agent Systems: Technologies and Applications 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92031-3_4.

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Mathieu, Philippe, and Sébastien Picault. "The Galaxian Project: A 3D Interaction-Based Animation Engine." In Advances on Practical Applications of Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38073-0_35.

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Lee, Wonki, and DaeEun Kim. "Local Interaction of Agents for Division of Labor in Multi-agent Systems." In From Animals to Animats 14. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43488-9_5.

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Actas de conferencias sobre el tema "Interaction multi-agents"

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Bazargani, Jalal Safari, and Soo-Mi Choi. "Multi-modal Interaction with Virtual Agents: A Pick-up and Placement Case Study." In 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2025. https://doi.org/10.1109/vrw66409.2025.00336.

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Zhao, Yuze, Yang Li, Feng Wu, and Linjun Shi. "Multi-Agents Integrated Energy Systems Optimal Scheduling Considering Carbon Allowance and Green Certificate Interaction." In 2024 4th Power System and Green Energy Conference (PSGEC). IEEE, 2024. http://dx.doi.org/10.1109/psgec62376.2024.10721137.

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Yahyaoui, Mohamed, Mouaad Oujabour, Leila Ben Letaifa, and Amine Bohi. "Multi-Face Emotion Detection for Effective Human-Robot Interaction." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013170300003890.

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Gunawardhana, Bhasura S., Yunxiang Zhang, Qi Sun, and Zhigang Deng. "Toward User-Aware Interactive Virtual Agents: Generative Multi-Modal Agent Behaviors in VR." In 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 2024. http://dx.doi.org/10.1109/ismar62088.2024.00123.

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Leeftink, Basten, Britta Abbink Spaink, Tomasz Zurek, and Tom Van Engers. "A Computational Model of Trustworthiness: Trust-Based Interactions Between Agents in Multi Agent System." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013152500003890.

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Karatas, Nihan, Shintaro Tamura, Momoko Fushiki, and Michio Okada. "Multi-party Conversation of Driving Agents." In HAI '18: 6th International Conference on Human-Agent Interaction. ACM, 2018. http://dx.doi.org/10.1145/3284432.3284466.

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Fang, Shu, Qipeng Liu, and Xiaofan Wang. "Swarming of multi-agents with topological-based random interaction." In 2013 Chinese Automation Congress (CAC). IEEE, 2013. http://dx.doi.org/10.1109/cac.2013.6775795.

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Ren, Tianyu, and Xiao-Jun Zeng. "Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/22.

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The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework
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Shi Guodong and Hong Yiguang. "Multi-agent coordination with switching interaction structures and heterogeneous agents." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605540.

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Maeedi, Ali, Muhammad Umer Khan, and Bulent Irfanoglu. "Reciprocal Altruism-based Path Planning Optimization for Multi-Agents." In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022. http://dx.doi.org/10.1109/hora55278.2022.9799828.

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Informes sobre el tema "Interaction multi-agents"

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99mTc SPECT-CT, Consensus QIBA Profile. Chair Yuni Dewaraja and Robert Miyaoka. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2019. https://doi.org/10.1148/qiba/20191021.

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The quantification of 99mTc labeled biomarkers can add unique value in many different settings, ranging from clinical trials of investigation new drugs to the treatment of individual patients with marketed therapeutics. For example, goals of precision medicine include using companion radiopharmaceutical diagnostics as just-in-time, predictive biomarkers for selecting patients to receive targeted treatments, customizing doses of internally administered radiotherapeutics, and assessing responses to treatment. This Profile describes quantitative outcome measures that represent proxies of target c
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