An introduction to computational learning theory
An introduction to computational learning theory
Learning sparse multivariate polynomials over a field with queries and counterexamples
Journal of Computer and System Sciences
iBundle: an efficient ascending price bundle auction
Proceedings of the 1st ACM conference on Electronic commerce
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Machine Learning
Machine Learning
Iterative Combinatorial Auctions: Theory and Practice
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Applying learning algorithms to preference elicitation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Preference Elicitation and Query Learning
The Journal of Machine Learning Research
On the computational power of iterative auctions
Proceedings of the 6th ACM conference on Electronic commerce
A modular framework for multi-agent preference elicitation
A modular framework for multi-agent preference elicitation
Bidding languages for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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We describe a modular elicitation framework for iterative combinatorial auctions. The framework includes proxy agents, each of which can adopt an individualized bidding language to represent partial value information of a bidder. The framework leverages algorithms from query learning to elicit value information via bids as the auction progresses. The approach reduces the multi-agent elicitation problem to isolated, single-agent learning problems, with competitive equilibrium prices used to facilitate auction clearing even without complete learning.