Applying learning algorithms to preference elicitation

Lahaie, Sébastien; Parkes, David C.

doi:10.1145/988772.988800

Cited by 56 publications

(52 citation statements)

References 21 publications

(37 reference statements)

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“…The idea is to allow multiple virtual rounds between proxy agents and the auction, and only fall back and ask for additional bids from suppliers when no progress is possible within the auction. Another recent idea is to use computational learning theory to generate elicitation queries (Lahaie and Parkes 2004). We also find it interesting to explore the role of hybrid auctions (Porter et al 2003, Ausubel andMilgrom 2004), with linear prices used in the early stage as a method to perform elicitation, coupled with a final one-shot stage.…”

Section: Discussionmentioning

confidence: 99%

Models for Iterative Multiattribute Procurement Auctions

2005

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M ultiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods.This paper provides an iterative auction design for an important special case of the multiattribute allocation problem with special (preferential independent) additive structure on the buyer value and seller costs. Auction Additive&Discrete provides a refined design for a price-based auction in which the price feedback decomposes to an additive part with a price for each attribute and an aggregate part that appears as a price discount for each supplier. In addition, this design also has excellent information revelation properties that are validated through computational experiments. The auction terminates with an outcome of a modified VickreyClarke-Groves mechanism. This paper also develops Auction NonLinear&Discrete for the more general nonlinear case-a particularly simple design that solves the general multiattribute allocation problem, but requires that the auctioneer maintains prices on bundles of attribute levels.

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Section: Discussionmentioning

confidence: 99%

Models for Iterative Multiattribute Procurement Auctions

2005

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“…Direct elicitation approaches, in which bidders respond to explicit queries about their valuations, have also been proposed for one-sided CAs (Conen & Sandholm, 2001;Hudson & Sandholm, 2004;Lahaie & Parkes, 2004;Lahaie, Constantin, & Parkes, 2005). Of particular relevance here are the ascending CAs that are designed to work with simple prices on items (Dunford, Hoffman, Menon, Sultana, & Wilson, 2003;Kwasnica, Ledyard, Porter, & DeMartini, 2005).…”

Section: Related Workmentioning

confidence: 99%

ICE: An Expressive Iterative Combinatorial Exchange

Lubin¹,

Juda²,

Cavallo³

et al.

Handbook of Spectrum Auction Design

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We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies.

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“…A number of positive results have been obtained along this line. For example, a bidder's valuation function can be elicited exactly with a number of value and demand queries that is polynomial in the length of the valuation function's representation in the XOR-language (Lahaie & Parkes 2004). Hence, valuations that admit a concise XOR-representation can be elicited efficiently using such queries.…”

Section: Combinatorial Auctionsmentioning

confidence: 99%

Comparing multiagent systems research in combinatorial auctions and voting

Conitzer

2010

Ann Math Artif Intell

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In a combinatorial auction, a set of resources is for sale, and agents can bid on subsets of these resources. In a voting setting, the agents decide among a set of alternatives by having each agent rank all the alternatives. Many of the key research issues in these two domains are similar. The aim of this paper is to give a convenient side-by-side comparison that will clarify the relation between the domains, and serve as a guide to future research.

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Applying learning algorithms to preference elicitation

Cited by 56 publications

References 21 publications

Models for Iterative Multiattribute Procurement Auctions

Models for Iterative Multiattribute Procurement Auctions

ICE: An Expressive Iterative Combinatorial Exchange

Comparing multiagent systems research in combinatorial auctions and voting

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