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Empirical game-theoretic analysis of the TAC Supply Chain game
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International Conference on Autonomous Agents archive
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems table of contents
Honolulu, Hawaii
SESSION: Mechanism design and game theory: full papers table of contents
Article No. 193  
Year of Publication: 2007
ISBN:978-81-904262-7-5
Authors
Patrick R. Jordan  University of Michigan, Ann Arbor, MI
Christopher Kiekintveld  University of Michigan, Ann Arbor, MI
Michael P. Wellman  University of Michigan, Ann Arbor, MI
Sponsor
: IFAAMAS
Publisher
ACM  New York, NY, USA
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ABSTRACT

The TAC Supply Chain Management (TAC/SCM) game presents a challenging dynamic environment for autonomous decision-making in a salient application domain. Strategic interactions complicate the analysis of games such as TAC/SCM. since the effectiveness of a given strategy depends on the strategies played by other agents on the supply chain. The TAC tournament generates results from one particular path of combinations, and success in the tournament is rightly regarded as evidence for agent quality. Such results along with post-competition controlled experiments provide useful evaluations of novel techniques employed in the game. We argue that a broader game-theoretic analysis framework can provide a firmer foundation for choice of experimental contexts. Exploiting a repository of agents from the 2005 and 2006 TAC/SCM tournaments, we demonstrate an empirical game-theoretic methodology based on extensive simulation and careful measurement. Our analysis of agents from TAC-05 reveals interesting interactions not seen in the tournament. Extending the analysis to TAC-06 enables us to measure progress from year-to-year, and generates a candidate empirical equilibrium among the best known strategies. We use this equilibrium as a stable background population for comparing relative performance of the 2006 agents, yielding insights complementing the tournament results.


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.

 
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CITED BY  11

Collaborative Colleagues:
Patrick R. Jordan: colleagues
Christopher Kiekintveld: colleagues
Michael P. Wellman: colleagues