More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 959-963.
Abstract
Query learning models from computational learning theory (CLT) can be adopted to perform elicitation in combinatorial auctions. Indeed, a recent elicitation framework demonstrated that the equivalence queries of CLT can be usefully simulated with price-based demand queries. In this paper, we validate the flexibility of this framework by defining a learning algorithm for atomic bidding languages, a class that includes XOR and OR. We also handle incentives, characterizing the communication requirements of the Vickrey-ClarkeGroves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium.
@inproceedings{ijcai2005p191,
title = {More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives},
author = {Lahaie, Sébastien and Constantin, Florin and Parkes, David C.},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {959--963},
year = {2005},
url = {https://ijcai.org/proceedings/2005/191},
}
TY - CPAPER TI - More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives AU - Lahaie, Sébastien AU - Constantin, Florin AU - Parkes, David C. T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 959 EP - 963 UR - https://ijcai.org/proceedings/2005/191 ER -Copied
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