Preference elicitation in combinatorial auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Partial-revelation VCG mechanism for combinatorial auctions
Eighteenth national conference on Artificial intelligence
Costly valuation computation in auctions
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Regret-based incremental partial revelation mechanisms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Autonomous Agents and Multi-Agent Systems
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Combinatorial auctions where agents can bid on bundles of items are desirable because they allow the agents to express complementarity and substitutability between the items. However, expressing one's preferences can require bidding on all bundles. Selective incremental preference elicitation by the auctioneer was recently proposed to address this problem but the idea was not experimentally validated. This paper evaluates several approaches and finds that in many cases, automated elicitation is in fact beneficial: as the number of items for sale increases, the amount of information elicited is a small and diminishing fraction of the information collected in traditional "direct revelation mechanisms" where bidders reveal all their valuation information. In forward auctions, the benefit is slightly reduced by increasing the number of agents, while in reverse auctions the benefit increases with both agents and items. The exception is with rank-based elicitors, which limit themselves to eliciting the next-best bundle: these do not scale as the number of agents grows.A full-length version of this paper is on-line at: http://www.cs.cmu.edu/~sandholm/generalizing03.pdf