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Record Nr. |
UNINA9910522920003321 |
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Titolo |
Distributed Artificial Intelligence : Third International Conference, DAI 2021, Shanghai, China, December 17–18, 2021, Proceedings / / edited by Jie Chen, Jérôme Lang, Christopher Amato, Dengji Zhao |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (256 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 13170 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Application software |
User interfaces (Computer systems) |
Human-computer interaction |
Computer science - Mathematics |
Computer engineering |
Computer networks |
Social sciences - Data processing |
Artificial Intelligence |
Computer and Information Systems Applications |
User Interfaces and Human Computer Interaction |
Mathematics of Computing |
Computer Engineering and Networks |
Computer Application in Social and Behavioral Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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The Power of Signaling and its Intrinsic Connection to the Price of Anarchy -- Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning -- SEIHAI: A Sample-effcient Hierarchical AI for the MineRL Competition -- GC: Multi-Agent Group Belief with Graph Clustering -- Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics -- Securities Based |
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Decision Markets -- MARL for Traffc Signal Control in Scenarios with Different Intersection Importance -- Safe Distributional Reinforcement Learning -- The Positive Effect of User Faults over Agent Perception in Collaborative Settings and its Use in Agent Design -- Behavioral Stable Marriage Problems -- FUN-Agent: a HUMAINE Competitor -- Signal Instructed Coordination in Cooperative Multi-Agent Reinforcement Learning -- A Description of the Jadescript Type System -- Combining M-MCTS and Deep Reinforcement Learning for General Game Playing -- A Two-Step Method for Dynamics of Abstract Argumentation. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the Third International Conference on Distributed Artificial Intelligence, DAI 2021, held in Shanghai, China, in December 2021. The 15 full papers presented in this book were carefully reviewed and selected from 31 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. |
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