Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets AMEC 2013, Saint Paul, MN, USA, May 6, 2013, TADA 2013, Bellevue, WA, USA, July 15, 2013, and AMEC and TADA 2014, Paris, France, May 5, 2014, Revised Selected Papers /

This volume contains 12 thoroughly refereed and revised papers detailing recent advances in research on designing trading agents and mechanisms for agent-mediated e-commerce. They were originally presented at three events: the Workshop on Agent-Mediated Electronic Commerce (AMEC 2013), co-located wi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Ceppi, Sofia (Editor ), David, Esther (Editor ), Podobnik, Vedran (Editor ), Robu, Valentin (Editor ), Shehory, Onn (Editor ), Stein, Sebastian (Editor ), Vetsikas, Ioannis A. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:Lecture Notes in Business Information Processing, 187
Temas:
Electronic commerce.
Business information services.
Artificial intelligence.
Information technology > Management.
Application software.
e-Commerce and e-Business.
IT in Business.
Artificial Intelligence.
Computer Application in Administrative Data Processing.
Computer and Information Systems Applications.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • An Analysis of Power Trading Agent Competition 2014
  • Towards Modeling Securities Markets as a Society of Heterogeneous Trading Agents
  • A Market-Based Coordinated Negotiation for QoS-Aware Service Selection
  • Optimal Agendas for Sequential Negotiations
  • An Empirical Analysis of QuiBids' Penny Auctions
  • Analysis of Fairness and Incentives of Profit Sharing Schemes in Group Buying
  • Distributed Prediction Markets Modeled by Weighted Bayesian Graphical Games
  • A Successful Broker Agent for Power TAC
  • Online Double Auction for Perishable Goods
  • Designing Tariffs in a Competitive Energy Market Using Particle Swarm Optimization Techniques
  • Classification Driven Detection of Opportunistic Bids in TAC SCM
  • An Intelligent Learning Mechanism for Trading Strategies for Local Energy Distribution.

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