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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.
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CITED BY 11
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Jinzhong Niu , Kai Cai , Simon Parsons , Enrico Gerding , Peter McBurney, Characterizing effective auction mechanisms: insights from the 2007 TAC market design competition, Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, May 12-16, 2008, Estoril, Portugal
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Christopher Kiekintveld , Jason Miller , Patrick R. Jordan , Lee F. Callender , Michael P. Wellman, Forecasting market prices in a supply chain game, Electronic Commerce Research and Applications, v.8 n.2, p.63-77, March, 2009
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Eric Sodomka , John Collins , Maria Gini, Efficient statistical methods for evaluating trading agent performance, Proceedings of the 22nd national conference on Artificial intelligence, p.770-775, July 22-26, 2007, Vancouver, British Columbia, Canada
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