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.
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.