Vidal's libraryTitle: | Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches |
Author: | Yuzo Fujishima, Kevin Leyton-Brown, and Yoav Shoham |
Book Tittle: | Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence |
Pages: | 548--553 |
Publisher: | Morgan Kaufmann Publishers Inc. |
Year: | 1999 |
Abstract: | In combinatorial auctions, multiple goods are sold simultaneously and bidders may bid for arbitrary combinations of goods. Determining the outcome of such an auction is an optimization problem that is NP-complete in the general case. We propose two methods of overcoming this apparent intractability. The first method, which is guaranteed to be optimal, reduces running time by structuring the search space so that a modified depth-first search usually avoids even considering allocations that contain conflicting bids. Caching and pruning are also used to speed searching. Our second method is a heuristic, market-based approach. It sets up a virtual multi-round auction in which a virtual agent represents each original bid bundle and places bids, according to a fixed strategy, for each good in that bundle. We show through experiments on synthetic data that (a) our first method finds optimal allocations quickly and offers good anytime performance, and (b) in many cases our second method, despite lacking guarantees regarding optimality or running time, quickly reaches solutions that are nearly optimal. |
Cited by 236 - Google Scholar
@InProceedings{fujishima99a,
author = {Yuzo Fujishima and Kevin Leyton-Brown and Yoav
Shoham},
title = {Taming the Computational Complexity of Combinatorial
Auctions: Optimal and Approximate Approaches},
googleid = {5ef8b4HHSAEJ:scholar.google.com/},
booktitle = {Proceedings of the Sixteenth International Joint
Conference on Artificial Intelligence},
year = 1999,
pages = {548--553},
publisher = {Morgan Kaufmann Publishers Inc.},
abstract = {In combinatorial auctions, multiple goods are sold
simultaneously and bidders may bid for arbitrary
combinations of goods. Determining the outcome of
such an auction is an optimization problem that is
NP-complete in the general case. We propose two
methods of overcoming this apparent
intractability. The first method, which is
guaranteed to be optimal, reduces running time by
structuring the search space so that a modified
depth-first search usually avoids even considering
allocations that contain conflicting bids. Caching
and pruning are also used to speed searching. Our
second method is a heuristic, market-based
approach. It sets up a virtual multi-round auction
in which a virtual agent represents each original
bid bundle and places bids, according to a fixed
strategy, for each good in that bundle. We show
through experiments on synthetic data that (a) our
first method finds optimal allocations quickly and
offers good anytime performance, and (b) in many
cases our second method, despite lacking guarantees
regarding optimality or running time, quickly
reaches solutions that are nearly optimal.},
keywords = {combinatorial auctions},
url = {http://jmvidal.cse.sc.edu/library/fujishima99a.pdf},
cluster = {92543151104649189}
}
Last modified: Wed Mar 9 10:14:43 EST 2011