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2000

Solving Combinatorial Auctions Using Stochastic Local Search

9 years 12 months ago
Solving Combinatorial Auctions Using Stochastic Local Search
Combinatorial auctions (CAs) have emerged as an important model in economics and show promise as a useful tool for tackling resource allocation in AI. Unfortunately, winner determination for CAs is NP-hard and recent algorithms have difficulty with problems involving goods and bids beyond the hundreds. We apply a new stochastic local search algorithm, Casanova, to this problem, and demonstrate that it finds high quality (even optimal) solutions much faster than recently proposed methods (up to several orders of magnitude), particularly for large problems. We also propose a logical language for naturally expressing combinatorial bids in which a single logical bid corresponds to a large (often exponential) number of explicit bids. We show that Casanova performs much better than systematic methods on such problems.
Holger H. Hoos, Craig Boutilier
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where AAAI
Authors Holger H. Hoos, Craig Boutilier
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