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<p>As the military continues to expand its use of intelligent agents in a variety of  operational aspects, event prediction and learning algorithms are becoming more and  more important. In this paper, we conduct a detailed analysis of two such algorithms:  Variable Order Markov and Look-Up Table models. Each model employs different  parameters for prediction, and this study attempts to determine which model is more  accurate in its prediction and why. We find the models contrast in that the Variable  Order Markov Model increases its average prediction probability, our primary  performance measure, with increased maximum model order, while the Look-Up Table  Model decreases average prediction probability with increased recency time threshold. In  addition, statistical tests of results of each model indicate a consistency in each model's  prediction capabilities, and most of the variation in the results could be explained by  model parameters.
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<br>Subjects: Algorithms; Prediction theory; Markov processes</div>
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Poor, Kristopher D.</div>
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