More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
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CitationLahaie, Sébastien, Florin Constantin, and David C. Parkes. 2005. More on the power of demand queries in combinatorial auctions: Learning atomic languages and handling incentives. In proceedings of the Nineteenth International Joint Conference on Artificial Intelligence: July 30-August 5, 2005, Edinburgh, Scotland, ed. A. Saffiotti, L. Pack Kaelbling, 959-964. Denver, C.O.: International Joint Conferences on Artificial Intelligence.
AbstractQuery 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-Clarke-Groves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4031553
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