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

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

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

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

Paperback(2004)

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Overview

This book constitutes the thoroughly refereed post-proceedings of the 5th International Workshop on Agent-Mediated Electronic Commerce, AMEC 2003, held in Melbourne, Australia in July 2003 as part of AAMAS 2003.

The 9 revised full papers presented were carefully selected from 22 submissions during two rounds of reviewing and revision. The papers are organized in topical sections on automated negotiation, systems and mechanism design, and multi-agent markets.


Product Details

ISBN-13: 9783540226741
Publisher: Springer Berlin Heidelberg
Publication date: 11/23/2004
Series: Lecture Notes in Computer Science , #3048
Edition description: 2004
Pages: 155
Product dimensions: 6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

Section I: Automated Negotiation.- Automated Negotiation and Bundling of Information Goods.- Two Sk-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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