Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets (eBook, PDF)

AMEC/TADA 2015, Istanbul, Turkey, May 4, 2015, and AMEC/TADA 2016, New York, NY, USA, July 10, 2016, Revised Selected Papers
Redaktion: Ceppi, Sofia; Vetsikas, Ioannis A.; Robu, Valentin; Hajaj, Chen; David, Esther

• Format: PDF

Produktbeschreibung

This book constitutes revised selected papers from the 17th and 18^th International Workshop on Agent-Mediated Electronic Commerce, AMEC TADA 2015 and 2016, which took place in Istanbul, Turkey, in May 2015, and in New York City, USA, in July 2016. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in the book. Both workshops aim to present a cross-section of the state of the art in automated electronic markets and encourage theoretical and empirical work that deals with both the individual agent level as well as the system level. Given the breadth of research topics in this field, the range of topics addressed in these papers is correspondingly broad. They range from papers that study theoretical issues, related to the design of interaction protocols and marketplaces, to the design and analysis of automated trading strategies used by individual agents - which are often developed as part of an entry to one of the tracks of the Trading Agents Competition.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 157
• Erscheinungstermin: 22. Februar 2017
• Englisch
• ISBN-13: 9783319542294
• Artikelnr.: 53063980

Inhaltsangabe

Strategic Free Information Disclosure for a Vickrey Auction.- On Revenue-Maximizing Walrasian Equilibria for Multi-Minded Bidders.- Extending Parking Lots with Electricity Trading Agent Functionalities.- Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing.- An Effective Broker for the Power TAC 2014.- Now, Later, or Both: a Closed-Form Optimal Decision for a Risk-Averse Buyer.- Investigation of Learning Strategies for the SPOT Broker in Power TAC.- On the Use of Off-the-Shelf Machine Learning Techniques to Predict Energy Demands of Power TAC Consumers.- A Genetic Algorithmic Approach to Automated Auction Mechanism Design.- AgentUDE: The Success Story of the Power TAC 2014's Champion.