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 / [electronic resource] :
edited by Peyman Faratin, David C. Parkes, Juan A. RodrÃguez-Aguilar, William E. Walsh.
- 1st ed. 2004.
- VII, 153 p. online resource.
- Lecture Notes in Artificial Intelligence, 3048 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 3048 .
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.
9783540259473
10.1007/b99040 doi
Artificial intelligence.
Econometrics.
Electronic commerce.
Artificial Intelligence.
Quantitative Economics.
e-Commerce and e-Business.
Q334-342 TA347.A78
006.3
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.
9783540259473
10.1007/b99040 doi
Artificial intelligence.
Econometrics.
Electronic commerce.
Artificial Intelligence.
Quantitative Economics.
e-Commerce and e-Business.
Q334-342 TA347.A78
006.3