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ATTac-2000: an adaptive autonomous bidding agent
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Source International Conference on Autonomous Agents archive
Proceedings of the fifth international conference on Autonomous agents table of contents
Montreal, Quebec, Canada
Pages: 238 - 245  
Year of Publication: 2001
ISBN:1-58113-326-X
Authors
Peter Stone  AT&T Labs -- Research, 180 Park Ave., Florham Park, NJ
Michael L. Littman  AT&T Labs -- Research, 180 Park Ave., Florham Park, NJ
Satinder Singh  AT&T Labs -- Research, 180 Park Ave., Florham Park, NJ
Michael Kearns  AT&T Labs -- Research, 180 Park Ave., Florham Park, NJ
Sponsor
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 12,   Downloads (12 Months): 32,   Citation Count: 10
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ABSTRACT

The First Trading Agent Competition (TAC) was held from June 22 to July 8, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to motivate researchers to apply unique approaches to a common task. This paper describes \attac, the first-place finisher in TAC. \attac\ uses a principled bidding strategy that includes several elements of {adaptivity\/}. In addition to the success at the competition, isolated empirical results are presented indicating the robustness and effectiveness of \attac's adaptive strategy.


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
P. C.Cramton. The FCC spectrum auctions: An early assessment. Journal of Economics and Management Strategy, 6(3):431-495, 1997.
 
2
J. A. Csirik, M. L. Littman, S. Singh, and P. Stone. FAucS: An FCC spectrum auction simulator for autonomous bidding agents. Under Review, 2001. Available at http: //www.research.att.com/~pstone/papers.html.
 
3
A. Eisenberg. In online auctions of the future, it'll be bot vs. bot vs. bot. The New York Times, 2000. August 17th.
 
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M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the Eleventh International Conference on Machine Learning, pages 157-163, San Mateo, CA, 1994. Morgan Kaufman.
 
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R. J. Weber. Making more from less: Strategic demand reduction in the FCC spectrum auctions. Journal of Economics and Management Strategy, 6(3):529-548, 1997.
 
10
M. P. Wellman, P. R. Wurman, K. O'Malley, R. Bangera, S.-d. Lin, D. Reeves, and W. E. Walsh. A trading agent competition. In press. Available at http://tac.eecs.umich.edu/reports.html, 2001.

CITED BY  10

Collaborative Colleagues:
Peter Stone: colleagues
Michael L. Littman: colleagues
Satinder Singh: colleagues
Michael Kearns: colleagues