Visualization and analysis methods for comparing agent behavior in TAC SCM

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Visualization and analysis methods for comparing agent behavior in TAC SCM

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International Conference on Autonomous Agents archive
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 table of contents

Budapest, Hungary

SESSION: Comprehensive/cross-cutting table of contents

Pages 1367-1368

Year of Publication: 2009

ISBN:978-0-9817381-7-8

Authors

William Groves	University of Minnesota
John Collins	University of Minnesota
Maria Gini	University of Minnesota

Sponsors

: The Foundation for Intelligent Physical Agents
Microsoft Research : Microsoft Research
: Whitestein Technologies
: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
: Drexel University
: Wiley -- Blackwell Ltd

Publisher

International Foundation for Autonomous Agents and Multiagent Systems Richland, SC

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ABSTRACT

We are targeting for analysis a complex, multi-agent, repeated market simulation in which agents compete to maximize overall profit over a finite time horizon. The Trading Agent Competition for Supply Chain Management (TAC SCM), which has been run annually as a tournament for five years, is designed to model realistic supply chain and market mechanisms. In this paper, we introduce our extensible analysis framework that can be used for posing and answering new questions related to understanding market mechanisms and agent behavior.

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	M. Benisch, J. Andrews, D. Bangerter, T. Kirchner, B. Tsai, and N. Sadeh. Cmieux supply chain trading analysis and instrumentation toolkit. Technical Report CMU-ISRI-05-127, Carnegie Mellon University, 2005.
	2	E. Sodomka, J. Collins, and M. L. Gini. Efficient statistical methods for evaluating trading agent performance. In AAAI, pages 770--775. AAAI Press, 2007.