An analysis of learning algorithms in complex stochastic environments
Loading...
Authors
Poor, Kristopher D.
Subjects
Advisors
Darken, Christian
Date of Issue
2007-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
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.
Type
Thesis
Description
Series/Report No
Department
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funding
Format
xiv, 49 p. ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.