LEARNING AND PREDICTION OF RELATIONAL TIME SERIES
dc.contributor.advisor | Darken, Christian | |
dc.contributor.author | Tan, Kian-Moh Terence | |
dc.date | Mar-13 | |
dc.date.accessioned | 2013-05-08T20:42:54Z | |
dc.date.available | 2013-05-08T20:42:54Z | |
dc.date.issued | 2013-03 | |
dc.identifier.uri | https://hdl.handle.net/10945/32907 | |
dc.description.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. | en_US |
dc.description.uri | http://archive.org/details/learningandpredi1094532907 | |
dc.publisher | Monterey, California. Naval Postgraduate School | en_US |
dc.title | LEARNING AND PREDICTION OF RELATIONAL TIME SERIES | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Computer Science | |
dc.subject.author | Relational time series | en_US |
dc.subject.author | learning | en_US |
dc.subject.author | prediction | en_US |
dc.subject.author | conceptual blending | en_US |
dc.subject.author | Cyber intrusion alert | en_US |
dc.description.service | Civilian, Singapore | en_US |
etd.thesisdegree.name | Doctor Of Philosophy In Modeling, Virtual Environments And Simulation (Moves) | en_US |
etd.thesisdegree.level | Doctoral | en_US |
etd.thesisdegree.discipline | Modeling, Virtual Environments, and Simulation Institute (MOVES) | en_US |
dc.description.distributionstatement | Approved for public release; distribution is unlimited. |
Files in this item
This item appears in the following Collection(s)
-
1. Thesis and Dissertation Collection, all items
Publicly releasable NPS Theses, Dissertations, MBA Professional Reports, Joint Applied Projects, Systems Engineering Project Reports and other NPS degree-earning written works.