C Kiekintveld, J Miller, PR Jordan, and MP Wellman
In Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1318–1325, May 2007.
Copyright (c) 2007, IFAAMAS. This is the author's version of the work.
It is posted here by permission of ACM for personal use, not for redistribution.
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Abstract
Future market conditions can be a pivotal factor in making business
decisions. We present and evaluate methods used by our agent,
Deep Maize, to forecast market prices in the Trading Agent Competition
Supply Chain Management Game. As a guiding principle
we seek to exploit as many sources of available information as possible
to inform predictions. Since information comes in several different
forms, we integrate well-known methods in a novel way to
make predictions. The core of our predictor is a nearest-neighbors
machine learning algorithm that identifies historical instances with
similar economic indicators. We augment this with an online learning
procedure that transforms the predictions by optimizing a scoring
rule. This allows us to select more relevant historical contexts
using additional information available during individual games. We
also explore the advantages of two different representations for predicting
price distributions. One uses absolute prices, and the other
uses statistics of price time series to exploit local stability. We evaluate
these methods using both data from the 2005 tournament final
round and additional simulations. We compare several variations
of our predictor to one another and a baseline predictor similar to
those used by many other tournament agents. We show substantial
improvements over the baseline predictor, and demonstrate that
each element of our predictor contributes to improved performance.
