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Artykuły w czasopismach na temat "Multiagent decision"
Kumar, Akshat, Shlomo Zilberstein i Marc Toussaint. "Probabilistic Inference Techniques for Scalable Multiagent Decision Making". Journal of Artificial Intelligence Research 53 (29.06.2015): 223–70. http://dx.doi.org/10.1613/jair.4649.
Pełny tekst źródłaHan, Xiaoyu. "Application of Reinforcement Learning in Multiagent Intelligent Decision-Making". Computational Intelligence and Neuroscience 2022 (16.09.2022): 1–6. http://dx.doi.org/10.1155/2022/8683616.
Pełny tekst źródłaNarayanan, Lakshmi Kanthan, Suresh Sankaranarayanan, Joel J. P. C. Rodrigues i 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, nr 3 (lipiec 2020): 52–79. http://dx.doi.org/10.4018/ijiit.2020070103.
Pełny tekst źródłaXiang, Yang, i 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, nr 02 (kwiecień 2015): 149–91. http://dx.doi.org/10.1142/s0218488515500075.
Pełny tekst źródłaXIANG, YANG, i FRANK HANSHAR. "MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, nr 06 (grudzień 2011): 939–76. http://dx.doi.org/10.1142/s0218488511007416.
Pełny tekst źródłaNunes, Ernesto, Julio Godoy i Maria Gini. "Multiagent Decision Making on Transportation Networks". Journal of Information Processing 22, nr 2 (2014): 307–18. http://dx.doi.org/10.2197/ipsjjip.22.307.
Pełny tekst źródłaMaturo, Antonio, i Aldo G. S. Ventre. "Reaching consensus in multiagent decision making". International Journal of Intelligent Systems 25, nr 3 (marzec 2010): 266–73. http://dx.doi.org/10.1002/int.20401.
Pełny tekst źródłaHe, Liu, Haoning Xi, Tangyi Guo i Kun Tang. "A Generalized Dynamic Potential Energy Model for Multiagent Path Planning". Journal of Advanced Transportation 2020 (24.07.2020): 1–14. http://dx.doi.org/10.1155/2020/1360491.
Pełny tekst źródłaXu, Yang, Xiang Li i 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.
Pełny tekst źródłaSzymak, Piotr. "Comparison of Centralized, Dispersed and Hybrid Multiagent Control Systems of Underwater Vehicles Team". Solid State Phenomena 180 (listopad 2011): 114–21. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.114.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaMaking 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.
Pełny tekst źródłaSosnowski, 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.
Pełny tekst źródłaStamatopoulou, 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.
Pełny tekst źródłaStamatopoulou, 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.
Pełny tekst źródłaWarden, Tobias [Verfasser], Otthein [Akademischer Betreuer] Herzog, Otthein [Gutachter] Herzog i 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.
Pełny tekst źródłaBarfuss, Wolfram. "Learning dynamics and decision paradigms in social-ecological dilemmas". Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20127.
Pełny tekst źródłaCollective 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.
Pełny tekst źródłaLes 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.
Pełny tekst źródłaPublic 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.
Pełny tekst źródłaKsiążki na temat "Multiagent decision"
Ventre, Aldo G. S., Antonio Maturo, Šárka Hošková-Mayerová i Janusz Kacprzyk, red. 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.
Pełny tekst źródłaVentre, Aldo G. S. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Znajdź pełny tekst źródłaAnders, Rantzer, i SpringerLink (Online service), red. Distributed Decision Making and Control. London: Springer London, 2012.
Znajdź pełny tekst źródłaJane, Doan, red. Choosing to learn: Ownership and responsibility in a primary multiage classroom. Portsmouth, NH: Heinemann, 1996.
Znajdź pełny tekst źródłaProbabilistic Reasoning in Multiagent Systems. Cambridge University Press, 2002.
Znajdź pełny tekst źródłaMaturo, Antonio, Aldo G. S. Ventre i Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.
Znajdź pełny tekst źródłaMulticriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2013.
Znajdź pełny tekst źródłaKacprzyk, Janusz, Antonio Maturo, Aldo G. S. Ventre i Šárka Hošková-Mayerová. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences. Springer, 2015.
Znajdź pełny tekst źródłaXiang, Yang. Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2010.
Znajdź pełny tekst źródłaProbabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2002.
Znajdź pełny tekst źródłaCzęści książek na temat "Multiagent decision"
Schröter, Kay, i Diemo Urbig. "C-IPS: Specifying Decision Interdependencies in Negotiations". W Multiagent System Technologies, 114–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30082-3_9.
Pełny tekst źródłaXiang, Yang, i Frank Hanshar. "Multiagent Decision by Partial Evaluation". W Advances in Artificial Intelligence, 242–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30353-1_21.
Pełny tekst źródłaBrandt, Felix. "Tournament Solutions and Their Applications to Multiagent Decision Making". W Multiagent System Technologies, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16178-0_1.
Pełny tekst źródłaRichter, Jan, Matthias Klusch i Ryszard Kowalczyk. "Monotonic Mixing of Decision Strategies for Agent-Based Bargaining". W Multiagent System Technologies, 113–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24603-6_12.
Pełny tekst źródłaLópez, Beatriz, Carles Pous, Pablo Gay i Albert Pla. "Multi Criteria Decision Methods for Coordinating Case-Based Agents". W Multiagent System Technologies, 54–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_6.
Pełny tekst źródłaSchwaiger, Arndt, i Björn Stahmer. "SimMarket: Multiagent-Based Customer Simulation and Decision Support for Category Management". W Multiagent System Technologies, 74–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39869-1_7.
Pełny tekst źródłaNg, Zhan Sheng, Aaron Yu Siang Tan, Arief Adhitya i Rajagopalan Srinivasan. "Agent-Based Model for Decision Support in Multi-Site Manufacturing Enterprises". W Multiagent System Technologies, 103–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_10.
Pełny tekst źródłaGuttmann, Christian. "Towards a Taxonomy of Decision Making Problems in Multi-Agent Systems". W Multiagent System Technologies, 195–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04143-3_19.
Pełny tekst źródłaWidmer, Tobias, i Marc Premm. "Agent-Based Decision Support for Allocating Caregiving Resources in a Dementia Scenario". W Multiagent System Technologies, 233–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27343-3_13.
Pełny tekst źródłaGüneş, Taha D., Timothy J. Norman i Long Tran-Thanh. "Budget Limited Trust-Aware Decision Making". W Autonomous Agents and Multiagent Systems, 101–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71679-4_7.
Pełny tekst źródłaStreszczenia konferencji na temat "Multiagent decision"
Campbell, Trevor, Luke Johnson i Jonathan P. How. "Multiagent allocation of Markov decision process tasks". W 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580186.
Pełny tekst źródłaXuan, Ping. "Modeling plan coordination in multiagent decision processes". W the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329396.
Pełny tekst źródłaRigopoulos, G., J. Psarras i N. V. Karadimas. "A Multiagent Model For Group Decision Support". W 21st Conference on Modelling and Simulation. ECMS, 2007. http://dx.doi.org/10.7148/2007-0096.
Pełny tekst źródłaYucelen, Tansel, i John Daniel Peterson. "Active-passive networked multiagent systems". W 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040479.
Pełny tekst źródłaKumar, Akshat. "Multiagent Decision Making and Learning in Urban Environments". W 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.
Pełny tekst źródłaCzibula, Gabriela, Adriana Mihaela Guran, Grigoreta Sofia Cojocar i Istvan Gergely Czibula. "Multiagent Decision Support Systems based on Supervised Learning". W 2008 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2008). IEEE, 2008. http://dx.doi.org/10.1109/aqtr.2008.4588943.
Pełny tekst źródłaColeman, Norman, Ching-Fang Lin, Jianhua Ge i Sarah Braasch. "Intelligent multiagent modeling and decision system for battlefield". W 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.
Pełny tekst źródłaTounsi, Jihene, Julien Boissiere i Georges Habchi. "Multiagent Decision Making For SME Supply Chain Simulation". W 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0210.
Pełny tekst źródłaTounsi, Jihene, Julien Boissiere i Georges Habchi. "Multiagent Decision Making For SME Supply Chain Simulation". W 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0203-0211.
Pełny tekst źródłaUzhva, Denis, Oleg Granichin i Olga Granichina. "Compressed Cluster Sensing in Multiagent IoT Control". W 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992703.
Pełny tekst źródłaRaporty organizacyjne na temat "Multiagent decision"
David, Gabrielle, D. Somerville, Julia McCarthy, Spencer MacNeil, Faith Fitzpatrick, Ryan Evans i 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.), październik 2021. http://dx.doi.org/10.21079/11681/42182.
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