Littérature scientifique sur le sujet « Multiagent decision »
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Articles de revues sur le sujet "Multiagent decision"
Kumar, Akshat, Shlomo Zilberstein et Marc Toussaint. « Probabilistic Inference Techniques for Scalable Multiagent Decision Making ». Journal of Artificial Intelligence Research 53 (29 juin 2015) : 223–70. http://dx.doi.org/10.1613/jair.4649.
Texte intégralHan, Xiaoyu. « Application of Reinforcement Learning in Multiagent Intelligent Decision-Making ». Computational Intelligence and Neuroscience 2022 (16 septembre 2022) : 1–6. http://dx.doi.org/10.1155/2022/8683616.
Texte intégralNarayanan, Lakshmi Kanthan, Suresh Sankaranarayanan, Joel J. P. C. Rodrigues et Pascal Lorenz. « Multi-Agent-Based Modeling for Underground Pipe Health and Water Quality Monitoring for Supplying Quality Water ». International Journal of Intelligent Information Technologies 16, no 3 (juillet 2020) : 52–79. http://dx.doi.org/10.4018/ijiit.2020070103.
Texte intégralXiang, Yang, et Frank Hanshar. « Multiagent Decision Making in Collaborative Decision Networks by Utility Cluster Based Partial Evaluation ». International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no 02 (avril 2015) : 149–91. http://dx.doi.org/10.1142/s0218488515500075.
Texte intégralXIANG, YANG, et FRANK HANSHAR. « MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS ». International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no 06 (décembre 2011) : 939–76. http://dx.doi.org/10.1142/s0218488511007416.
Texte intégralNunes, Ernesto, Julio Godoy et Maria Gini. « Multiagent Decision Making on Transportation Networks ». Journal of Information Processing 22, no 2 (2014) : 307–18. http://dx.doi.org/10.2197/ipsjjip.22.307.
Texte intégralMaturo, Antonio, et Aldo G. S. Ventre. « Reaching consensus in multiagent decision making ». International Journal of Intelligent Systems 25, no 3 (mars 2010) : 266–73. http://dx.doi.org/10.1002/int.20401.
Texte intégralHe, Liu, Haoning Xi, Tangyi Guo et Kun Tang. « A Generalized Dynamic Potential Energy Model for Multiagent Path Planning ». Journal of Advanced Transportation 2020 (24 juillet 2020) : 1–14. http://dx.doi.org/10.1155/2020/1360491.
Texte intégralXu, Yang, Xiang Li et Ming Liu. « Modeling and Simulation of Complex Network Attributes on Coordinating Large Multiagent System ». Scientific World Journal 2014 (2014) : 1–15. http://dx.doi.org/10.1155/2014/412479.
Texte intégralSzymak, Piotr. « Comparison of Centralized, Dispersed and Hybrid Multiagent Control Systems of Underwater Vehicles Team ». Solid State Phenomena 180 (novembre 2011) : 114–21. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.114.
Texte intégralThèses sur le sujet "Multiagent decision"
Burkov, Andriy. « Leveraging Repeated Games for Solving Complex Multiagent Decision Problems ». Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28028/28028.pdf.
Texte intégralMaking good decisions in multiagent environments is a hard problem in the sense that the presence of several decision makers implies conflicts of interests, a lack of coordination, and a multiplicity of possible decisions. If, then, the same decision makers interact continuously through time, they have to decide not only what to do in the present, but also how their present decisions may affect the behavior of the others in the future. Game theory is a mathematical tool that aims to model such interactions as strategic games of multiple players. Therefore, multiagent decision problems are often studied using game theory. In this context, and being restricted to dynamic games, complex multiagent decision problems can be algorithmically approached. The contribution of this thesis is three-fold. First, this thesis contributes an algorithmic framework for distributed planning in non-cooperative dynamic games. The multiplicity of possible plans is a matter of serious complications for any planning approach. We propose a novel approach based on the concept of learning in repeated games. Our approach permits overcoming the aforementioned complications by means of communication between players. We then propose a learning algorithm for repeated game self-play. Our algorithm allows players to converge, in an initially unknown repeated game, to a joint behavior optimal in a certain, well-defined sense, without communication between players. Finally, we propose a family of algorithms for approximately solving dynamic games, and for extracting equilibrium strategy profiles. In this context, we first propose a method to compute a nonempty subset of approximate subgame-perfect equilibria in repeated games. We then demonstrate how to extend this method for approximating all subgame-perfect equilibria in repeated games, and also for solving more complex dynamic games.
Gasparini, Luca. « Severity sensitive norm analysis and decision making ». Thesis, University of Aberdeen, 2017. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=231873.
Texte intégralSosnowski, Scott T. « Approximate Action Selection For Large, Coordinating, Multiagent Systems ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459468867.
Texte intégralStamatopoulou, Anastasia. « AGGREGATION IN MULTIAGENT AND MULTICRITERIA DECISION MODELS : INTERACTION, DYNAMICS, AND MAXIMUM ENTROPY WEIGHTS IN THE FRAMEWORK OF CHOQUET INTEGRATION ». Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/311987.
Texte intégralStamatopoulou, Anastasia. « AGGREGATION IN MULTIAGENT AND MULTICRITERIA DECISION MODELS : INTERACTION, DYNAMICS, AND MAXIMUM ENTROPY WEIGHTS IN THE FRAMEWORK OF CHOQUET INTEGRATION ». Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/311987.
Texte intégralWarden, Tobias [Verfasser], Otthein [Akademischer Betreuer] Herzog, Otthein [Gutachter] Herzog et Winfried [Gutachter] Lamersdorf. « Interactive Multiagent Adaptation of Individual Classification Models for Decision Support / Tobias Warden ; Gutachter : Otthein Herzog, Winfried Lamersdorf ; Betreuer : Otthein Herzog ». Bremen : Staats- und Universitätsbibliothek Bremen, 2019. http://d-nb.info/1199003611/34.
Texte intégralBarfuss, Wolfram. « Learning dynamics and decision paradigms in social-ecological dilemmas ». Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20127.
Texte intégralCollective action is required to enter sustainable development pathways in coupled social-ecological systems, safely away from dangerous tipping elements. Without denying the usefulness of other model design principles, this thesis proposes the agent-environment interface as the mathematical foundation for the design of social-ecological system models. First, this work refines techniques from the statistical physics literature on learning dynamics to derive a deterministic limit of established reinforcement learning algorithms from artificial intelligence research. Illustrations of the resulting learning dynamics reveal a wide range of different dynamical regimes, such as fixed points, periodic orbits and deterministic chaos. Second, the derived multi-state learning equations are applied to a newly introduced environment, the Ecological Public Good. It models a coupled social-ecological dilemma, extending established repeated social dilemma games by an ecological tipping element. Known theoretical and empirical results are reproduced and novel qualitatively different parameter regimes are discovered, including one in which these reward-optimizing agents prefer to collectively suffer in environmental collapse rather than cooperating in a prosperous environment. Third, this thesis challenges the reward optimizing paradigm of the learning equations. It presents a novel formal comparison of the three decision paradigms of economic optimization, sustainability and safety for the governance of an environmental tipping element. It is shown that no paradigm guarantees fulfilling requirements imposed by another paradigm. Further, the absence of a master paradigm is shown to be of special relevance for governing the climate system, since the latter may reside at the edge between parameter regimes where economic welfare optimization becomes neither sustainable nor safe.
Serramia, Amoros Marc. « Value-aligned norm selection ». Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/672634.
Texte intégralLes normes s’utilitzen àmpliament en societats tant d'humans com d'agents per a regular les accions dels seus individus. Tanmateix, tot i que els legisladors poden estar considerant aspectes ètics de forma intrínseca en definir normes, aquests aspectes no són usualment considerats de forma explícita. Aquesta tesi avança l'estat de l'art en sistemes multiagent normatius formalitzant mètodes quantitatius i qualitatius per seleccionar normes basant-se en els valors morals i les preferències sobre aquests valors. Anomenem aquest procés: selecció de normes alineades als valors. L’aproximació quantitativa a la selecció de normes alineades als valors està basada en la literatura d'ètica. Arran de l'estudi de les relacions entre normes, accions i valors que es fa a la literatura, proposem una definició formal de les relacions entre accions i valors a través de les funcions de judici, i de les relacions entre normes i valors a través de les funcions de promoció. Utilitzem aquestes funcions per calcular l’alineament d’un conjunt de normes amb els valors. D'aquesta manera, la selecció de normes consisteix a trobar el conjunt de normes que maximitzin l’alineament amb els valors. Tot i que les resolucions basades en utilitats són comunes en la presa de decisions, especificar utilitats pot ser una tasca difícil o impossible. Per exemple, no és fàcil avaluar numèricament l'impacte d'una norma sobre un valor. En termes més generals, la selecció d’alguns elements d'un conjunt de candidats, sol estar guiada per criteris de decisió. Identifiquem aquesta família de problemes que anomenem problemes de selecció del conjunt dominant. Proposem dues resolucions per a aquests problemes depenent de com s'especifiquen les relacions entre els elements i els criteris de decisió. Les dues resolucions transformen les preferències sobre criteris en preferències sobre conjunts d'elements. Ho fem en dos passos: (i) transformem les preferències sobre criteris en preferències sobre elements; i (ii) transformem les preferències sobre elements en preferències sobre conjunts d'aquests elements. La solució és el conjunt més preferit. Com que el problema de selecció de normes és una instància de la família de problemes de selecció del conjunt dominant, podem adaptar aquestes resolucions per a la selecció de normes.
Vieira, Fábio Lopes. « UM SISTEMA MULTIAGENTE PARA APOIO AS DECISÕES NO PROCESSO DE LICITAÇÃO PÚBLICA ». Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/499.
Texte intégralPublic licitation is an administrative process which goal is to purchase goods or services to the sectors belonging to the public administration and follow the rules of law no. 8.666/93. In this process public officials need to take decisions such as choosing the type and modality of the licitation. Due to the complexity of the law governing the licitation process and the great possibilities of conducting the licitation process in different scenarios and also its dynamism in the face of constant changes in legislation, we developed a multi-agent system to optimize the decisions of those responsible for acquiring goods and services for the public administration. A Multiagent System is a system composed of several agents that communicate and are collectively capable of achieving goals that they would not be able to meet separately. The complexity of these systems is approached through interactions between agents. We used the exchange messages architecture, where agents communicate directly with each other through asynchronous messages using a chat protocol, which sets the rules and enforces the formalism necessary for messages to be sent and understood by the agents. To specify the system we adopted MADAE-Pro, a process which guides the development of multi-agent systems through the phases of specification, design and implementation.
A licitação pública é um processo administrativo cujo objetivo é a compra de bens ou serviços para os órgãos pertencentes à Administração Pública e segue as normas da lei nº. 8.666/93. Nesse processo há necessidade de que os agentes públicos envolvidos tomem decisões como a escolha do tipo e da modalidade da licitação. Devido a complexidade da Lei que regula o processo de licitação e às inúmeras possibilidades de condução do processo licitatório em diversos cenários e também a seu dinamismo, diante das constantes alterações na legislação; foi desenvolvido um sistema multiagente para o processo de licitação pública visando otimizar a tomada de decisões dos responsáveis pela aquisição dos bens e serviços na Administração Pública. Um Sistema Multiagente é um sistema composto por vários agentes que se comunicam e são coletivamente capazes de atingir objetivos que não seriam capazes de satisfazer separadamente. A complexidade destes sistemas é abordada através das interações entre os agentes, ou seja, cada agente pode executar, dentro de suas limitações, uma tarefa simples, mas a boa coordenação da execução dessas tarefas simples por cada agente torna o sistema capaz de executar tarefas de grande complexidade. Foi utilizada a arquitetura de troca de mensagem entre agentes, onde os agentes se comunicam diretamente uns com os outros, através de mensagens assíncronas, utilizando um protocolo de conversação, o qual dita as regras e impõe o formalismo necessário para que as mensagens sejam encaminhadas e compreendidas pelos agentes. Para fazer a especificação do sistema, adotou-se o MADAEPro, um processo que guia o desenvolvimento de um sistema multiagente nas fases de especificação, projeto e implementação.
Sotnik, Garry. « SOSIEL : a Cognitive, Multi-Agent, and Knowledge-Based Platform for Modeling Boundedly-Rational Decision-Making ». PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4239.
Texte intégralLivres sur le sujet "Multiagent decision"
Ventre, Aldo G. S., Antonio Maturo, Šárka Hošková-Mayerová et Janusz Kacprzyk, dir. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35635-3.
Texte intégralVentre, Aldo G. S. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013.
Trouver le texte intégralAnders, Rantzer, et SpringerLink (Online service), dir. Distributed Decision Making and Control. London : Springer London, 2012.
Trouver le texte intégralJane, Doan, dir. Choosing to learn : Ownership and responsibility in a primary multiage classroom. Portsmouth, NH : Heinemann, 1996.
Trouver le texte intégralProbabilistic Reasoning in Multiagent Systems. Cambridge University Press, 2002.
Trouver le texte intégralMaturo, Antonio, Aldo G. S. Ventre et Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.
Trouver le texte intégralMulticriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.
Trouver le texte intégralKacprzyk, Janusz, Antonio Maturo, Aldo G. S. Ventre et Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2015.
Trouver le texte intégralXiang, Yang. Probabilistic Reasoning in Multiagent Systems : A Graphical Models Approach. Cambridge University Press, 2010.
Trouver le texte intégralProbabilistic Reasoning in Multiagent Systems : A Graphical Models Approach. Cambridge University Press, 2002.
Trouver le texte intégralChapitres de livres sur le sujet "Multiagent decision"
Schröter, Kay, et Diemo Urbig. « C-IPS : Specifying Decision Interdependencies in Negotiations ». Dans Multiagent System Technologies, 114–25. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30082-3_9.
Texte intégralXiang, Yang, et Frank Hanshar. « Multiagent Decision by Partial Evaluation ». Dans Advances in Artificial Intelligence, 242–54. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30353-1_21.
Texte intégralBrandt, Felix. « Tournament Solutions and Their Applications to Multiagent Decision Making ». Dans Multiagent System Technologies, 1. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16178-0_1.
Texte intégralRichter, Jan, Matthias Klusch et Ryszard Kowalczyk. « Monotonic Mixing of Decision Strategies for Agent-Based Bargaining ». Dans Multiagent System Technologies, 113–24. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24603-6_12.
Texte intégralLópez, Beatriz, Carles Pous, Pablo Gay et Albert Pla. « Multi Criteria Decision Methods for Coordinating Case-Based Agents ». Dans Multiagent System Technologies, 54–65. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_6.
Texte intégralSchwaiger, Arndt, et Björn Stahmer. « SimMarket : Multiagent-Based Customer Simulation and Decision Support for Category Management ». Dans Multiagent System Technologies, 74–84. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39869-1_7.
Texte intégralNg, Zhan Sheng, Aaron Yu Siang Tan, Arief Adhitya et Rajagopalan Srinivasan. « Agent-Based Model for Decision Support in Multi-Site Manufacturing Enterprises ». Dans Multiagent System Technologies, 103–14. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_10.
Texte intégralGuttmann, Christian. « Towards a Taxonomy of Decision Making Problems in Multi-Agent Systems ». Dans Multiagent System Technologies, 195–201. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_19.
Texte intégralWidmer, Tobias, et Marc Premm. « Agent-Based Decision Support for Allocating Caregiving Resources in a Dementia Scenario ». Dans Multiagent System Technologies, 233–48. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27343-3_13.
Texte intégralGüneş, Taha D., Timothy J. Norman et Long Tran-Thanh. « Budget Limited Trust-Aware Decision Making ». Dans Autonomous Agents and Multiagent Systems, 101–10. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71679-4_7.
Texte intégralActes de conférences sur le sujet "Multiagent decision"
Campbell, Trevor, Luke Johnson et Jonathan P. How. « Multiagent allocation of Markov decision process tasks ». Dans 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580186.
Texte intégralXuan, Ping. « Modeling plan coordination in multiagent decision processes ». Dans the 6th international joint conference. New York, New York, USA : ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329396.
Texte intégralRigopoulos, G., J. Psarras et N. V. Karadimas. « A Multiagent Model For Group Decision Support ». Dans 21st Conference on Modelling and Simulation. ECMS, 2007. http://dx.doi.org/10.7148/2007-0096.
Texte intégralYucelen, Tansel, et John Daniel Peterson. « Active-passive networked multiagent systems ». Dans 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040479.
Texte intégralKumar, Akshat. « Multiagent Decision Making and Learning in Urban Environments ». Dans Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California : International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/895.
Texte intégralCzibula, Gabriela, Adriana Mihaela Guran, Grigoreta Sofia Cojocar et Istvan Gergely Czibula. « Multiagent Decision Support Systems based on Supervised Learning ». Dans 2008 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2008). IEEE, 2008. http://dx.doi.org/10.1109/aqtr.2008.4588943.
Texte intégralColeman, Norman, Ching-Fang Lin, Jianhua Ge et Sarah Braasch. « Intelligent multiagent modeling and decision system for battlefield ». Dans Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina : American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-3992.
Texte intégralTounsi, Jihene, Julien Boissiere et Georges Habchi. « Multiagent Decision Making For SME Supply Chain Simulation ». Dans 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0210.
Texte intégralTounsi, Jihene, Julien Boissiere et Georges Habchi. « Multiagent Decision Making For SME Supply Chain Simulation ». Dans 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0211.
Texte intégralUzhva, Denis, Oleg Granichin et Olga Granichina. « Compressed Cluster Sensing in Multiagent IoT Control ». Dans 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992703.
Texte intégralRapports d'organisations sur le sujet "Multiagent decision"
David, Gabrielle, D. Somerville, Julia McCarthy, Spencer MacNeil, Faith Fitzpatrick, Ryan Evans et David Wilson. Technical guide for the development, evaluation, and modification of stream assessment methods for the Corps Regulatory Program. Engineer Research and Development Center (U.S.), octobre 2021. http://dx.doi.org/10.21079/11681/42182.
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