Bültmann & Gerriets
Multi-Agent Systems
18th European Conference, EUMAS 2021, Virtual Event, June 28¿29, 2021, Revised Selected Papers
von Nimrod Talmon, Ariel Rosenfeld
Verlag: Springer International Publishing
Reihe: Lecture Notes in Artificial Intelligence Nr. 12802
Hardcover
ISBN: 978-3-030-82253-8
Auflage: 1st ed. 2021
Erschienen am 21.07.2021
Sprache: Englisch
Format: 235 mm [H] x 155 mm [B] x 16 mm [T]
Gewicht: 446 Gramm
Umfang: 292 Seiten

Preis: 53,49 €
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Klappentext
Inhaltsverzeichnis

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.



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.


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