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Agent-Mediated Electronic Commerce V [electronic resource] : Designing Mechanisms and Systems, AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15. 2003, Revised Selected Papers /

Contributor(s): Material type: TextTextSeries: Lecture Notes in Artificial Intelligence ; 3048Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004Edition: 1st ed. 2004Description: VII, 153 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540259473
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
Section I: Automated Negotiation -- Automated Negotiation and Bundling of Information Goods -- Two Stock-Trading Agents: Market Making and Technical Analysis -- Acquiring Tradeoff Preferences for Automated Negotiations: A Case Study -- A Decommitment Strategy in a Competitive Multi-agent Transportation Setting -- Section II: Mechanism Design -- Sequences of Take-It-or-Leave-It Offers: Near-Optimal Auctions Without Full Valuation Revelation -- Mechanism for Optimally Trading Off Revenue and Efficiency in Multi-unit Auctions -- Choosing Samples to Compute Heuristic-Strategy Nash Equilibrium -- Section III: Multi-agent Markets -- Improving Learning Performance by Applying Economic Knowledge -- Handling Resource Use Oscillation in Multi-agent Markets.
In: Springer Nature eBook
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Section I: Automated Negotiation -- Automated Negotiation and Bundling of Information Goods -- Two Stock-Trading Agents: Market Making and Technical Analysis -- Acquiring Tradeoff Preferences for Automated Negotiations: A Case Study -- A Decommitment Strategy in a Competitive Multi-agent Transportation Setting -- Section II: Mechanism Design -- Sequences of Take-It-or-Leave-It Offers: Near-Optimal Auctions Without Full Valuation Revelation -- Mechanism for Optimally Trading Off Revenue and Efficiency in Multi-unit Auctions -- Choosing Samples to Compute Heuristic-Strategy Nash Equilibrium -- Section III: Multi-agent Markets -- Improving Learning Performance by Applying Economic Knowledge -- Handling Resource Use Oscillation in Multi-agent Markets.

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