C Kiekintveld, J Miller, PR Jordan, LF Callender, and MP Wellman

Electronic Commerce Research and Applications 8:63–77, 2009.
Copyright (c) 2008, Elsevier BV.

Abstract

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.

Revised and extended version of a paper previously presented at the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1318-1325, May 2007.

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