LEARNING AND PREDICTION OF RELATIONAL TIME SERIES

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Authors
Tan, Kian-Moh Terence
Subjects
Relational time series
learning
prediction
conceptual blending
Cyber intrusion alert
Advisors
Darken, Christian
Date of Issue
2013-03
Date
Mar-13
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Prediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories conceptual blending theory and event segmentation theory. We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods. The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian techniques, and compared their prediction accuracy quantitatively for three domains a role-playing game, intrusion-system alerts, and event prediction of maritime paths in a discrete-event simulator. Other contributions include two new unification algorithms that improve over a nave one, and an exploration of ways to maintain a minimum-size knowledge base without affecting prediction accuracy.
Type
Thesis
Description
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Department
Computer Science
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Distribution Statement
Approved for public release; distribution is unlimited.
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