Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Designing the Market Game for a Trading Agent Competition
IEEE Internet Computing
FAucS: An FCC Spectrum Auction Simulator for Autonomous Bidding Agents
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Implicit Negotiation in Repeated Games
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Designing Bidding Strategies for Trading Agents in Electronic Auctions
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
The First International Trading Agent Competition: Autonomous Bidding Agents
Electronic Commerce Research
Towards a test‐bed for trading agents in electronic auction markets
AI Communications
ATTac-2001: A Learning, Autonomous Bidding Agent
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
A polynomial-time nash equilibrium algorithm for repeated games
Proceedings of the 4th ACM conference on Electronic commerce
A polynomial-time Nash equilibrium algorithm for repeated games
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Bidding in sealed-bid and English multi-attribute auctions
Decision Support Systems
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
Journal of Artificial Intelligence Research
A polynomial-time Nash equilibrium algorithm for repeated games
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
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Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has been significant research on auction theory, especially from the perspective of auction mechanisms, studies of autonomous bidding agents and their interactions are relatively few and recent. This paper examines several autonomous agent bidding strategies in the context of FAucS, a faithful simulation of a complex FCC spectrum auction. We introduce punishing randomized strategic demand reduction (PRSDR), a novel bidding strategy by which bidders can partition available goods in a mutually beneficial way without explicit inter-agent communication. When all use PRSDR, bidders obtain significantly better results than when using a reasonable baseline approach. The strategy automatically detects and punishes non-cooperating bidders to achieve robustness in the face of agent defection, and performs well under alternative conditions. The PRSDR strategy is fully implemented and we present detailed empirical results.