ACM Home Page
Please provide us with feedback. Feedback
Winner determination in combinatorial auction generalizations
Full text PdfPdf (216 KB)
Source International Conference on Autonomous Agents archive
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1 table of contents
Bologna, Italy
SESSION: Session 2A: markets and auctions I table of contents
Pages: 69 - 76  
Year of Publication: 2002
ISBN:1-58113-480-0
Authors
Tuomas Sandholm  Carnegie Mellon University, Pittsburgh, PA
Subhash Suri  University of California, Santa Barbara, CA
Andrew Gilpin  CombineNet, Inc., Pittsburgh, PA
David Levine  CombineNet, Inc., Pittsburgh, PA
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 16,   Downloads (12 Months): 55,   Citation Count: 35
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/544741.544760
What is a DOI?

ABSTRACT

Combinatorial markets where bids can be submitted on bundles of items can be economically desirable coordination mechanisms in multiagent systems where the items exhibit complementarity and substitutability. There has been a surge of research on winner determination in combinatorial auctions. In this paper we study a wider range of combinatorial market designs: auctions, reverse auctions, and exchanges, with one or multiple units of each item, with and without free disposal. We first theoretically characterize the complexity of finding a feasible, approximate, or optimal solution. Reverse auctions with free disposal can be approximated (even in the multi-unit case), although auctions cannot. When XOR-constraints between bids are allowed (to express substitutability), the hardness turns the other way around: even finding a feasible solution for a reverse auction or exchanges is &Ngr;&Pgr;-complete, while in auctions that is trivial. Finally, in all of the cases without free disposal, even finding a feasible solution is &Ngr;&Pgr;-complete.We then ran experiments on known benchmarks as well as ones which we introduced, to study the complexity of the market variants in practice. Cases with free disposal tended to be easier than ones without. On many distributions, reverse auctions with free disposal were easier than auctions with free disposal---as the approximability would suggest---but interestingly, on one of the most realistic distributions they were harder. Single-unit exchanges were easy, but multi-unit exchanges were extremely hard.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
A. Andersson, M. Tenhunen, and F. Ygge. Integer programming for combinatorial auction winner determination. ICMAS, p. 39--46, 2000.
 
2
S. de Vries and R. Vohra. Combinatorial auctions: A survey. Draft, August 28th, 2000.
 
3
 
4
M. R. Garey and D. S. Johnson. Computers and Intractability. W. H. Freeman and Company, 1979.
5
 
6
J. Håstad. Clique is hard to approximate within n1-ε. Acta Mathematica, 182:105--142, 1999.
 
7
 
8
 
9
A. Kothari, T. Sandholm, and S. Suri. Solving combinatorial exchanges: Optimality via few partial bids. AAAI workshop on AI for Business, 2002.
10
11
 
12
13
 
14
 
15
 
16
 
17
 
18
T. Sandholm and S. Suri. Side constraints and non-price attributes in markets. IJCAI Workshop on Distributed Constraint Reasoning, p. 55--61, 2001.
 
19
T. Sandholm, S. Suri, A. Gilpin, and D. Levine. CABOB: A fast optimal algorithm for combinatorial auctions. IJCAI, p. 1102--1108, 2001.
 
20

CITED BY  35

Collaborative Colleagues:
Tuomas Sandholm: colleagues
Subhash Suri: colleagues
Andrew Gilpin: colleagues
David Levine: colleagues