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Multi-Agent Systems: Lecture Notes in Computer Science, cartea 12802

Editat de Ariel Rosenfeld, Nimrod Talmon
en Limba Engleză Paperback – 21 iul 2021
This book constitutes the revised post-conference proceedings of the 18th European Conference on Multi-Agent Systems, EUMAS 2021. The conference was held online in June, 2021. 16 full papers are presented in this volume, each of which carefully reviewed and selected from a total of 51 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of multi-agent systems.
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Specificații

ISBN-13: 9783030822538
ISBN-10: 3030822532
Pagini: 292
Ilustrații: X, 281 p. 55 illus., 39 illus. in color.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.45 kg
Ediția:1st edition 2021
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Ascending-Price Mechanism for General Multi-Sided Markets.- Governing Black-Box Agents in Competitive Multi-Attribute MAS.- Planning in Non-Uniform Environments for Multi-Agent Pickup and Delivery Tasks.- Revealed preference argumentation framework and applications in consumer behaviour analyses.- Coordinating Multi-Party Vehicle Routing with Location Congestion via Iterative Best Response.- Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction.- Large-scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints.- Convention emergence with congested resources.- Aiming for Half Gets You to the Top: Winning PowerTAC 2020.- Parameterized Analysis of Assignment Under Multiple Preferences.- Frameworks and the Preservation of Solid Semantic Properties.- Verification of Multi-Layered Assignment Problems.- Logic and Model Checking by Imprecise Probabilistic Interpreted Systems.- On the Complexity of Predicting Election Outcomes and Estimating Their Robustness.- Point Based Solution Method for Communicative IPOMDPs.- A Decentralized Token-based Negotiation Approach for Multi-Agent Path Finding.