Artificial Intelligence Review - Special issue on lazy learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Online learning in online auctions
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Co-evolutionary Auction Mechanism Design: A Preliminary Report
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Applying evolutionary game theory to auction mechanism design
Proceedings of the 4th ACM conference on Electronic commerce
Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Developing adaptive auction mechanisms
ACM SIGecom Exchanges
Exploring auction databases through interactive visualization
Decision Support Systems
Learn while you earn: two approaches to learning auction parameters in take-it-or-leave-it auctions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Evolutionary dynamics for designing multi-period auctions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Learning the IPA market with individual and social rewards
Web Intelligence and Agent Systems
Setting discrete bid levels adaptively in repeated auctions
Proceedings of the 11th International Conference on Electronic Commerce
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge
INFORMS Journal on Computing
Sponsored search auctions: an overview of research with emphasis on game theoretic aspects
Electronic Commerce Research
Creating standardized products for electronic markets
Future Generation Computer Systems
A game- heoretic machine learning approach for revenue maximization in sponsored search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders often exhibit unknown and variable behavior, making them difficult to model and complicating the design process. To address this challenge, we explore the use of an adaptive auction mechanism: one that learns to adjust its parameters in response to past empirical bidder behavior so as to maximize an objective function such as auctioneer revenue. In this paper, we give an overview of our general approach and then present an instantiation in a specific auction scenario. In addition, we show how predictions of possible bidder behavior can be incorporated into the adaptive mechanism through a metalearning process. The approach is fully implemented and tested. Results indicate that the adaptive mechanism is able to outperform any single fixed mechanism, and that the addition of metalearning improves performance substantially.