Columbus State University
A Step Towards an Intelligent Digital Training Management System (I-DTMS)
Abstract
dc:description.abstract<p>The U.S. Army Digital Training Management System (DTMS) is a web-based system designed to create a single point of entry for units to schedule unit training, manage training resources, and create schedules and master calendars for training. Currently, the U.S. Army uses DTMS to manage unit training and help commanders at each step of the training management process from planning and preparing to execute and assessing the training plans. This research aims to add intelligent features to DTMS through augmenting it with an intelligent decision support system (ITPSS) that utilizes artificial intelligence techniques (case-based reasoning, in particular) to determine if training guidance (either annual training guidance or doctrinal template) was implemented correctly. The proposed system should also help company commanders to refine their unit training plans after reviewing previous similar unit training plans recommended or retrieved by the ITPSS. This research demonstrates how case-based reasoning could improve the training plan development and approval process in DTMS, and questionnaire results support this analysis. It is worth noting that the focus of this research is on the applicability and plausibility of the proposed decision system, not on developing an interface between DTMS and DSS.</p>
Degree
thesis:*- Name thesis:degree_name
- Computer Science - Applied Computing Track
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- TSYS School of Computer Science
- Year dc:date.available
- 2016
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Dugger, Andrew
- Contributors dc:contributor
-
- Rania Hodhod
- Rodrigo Obando
- Shamim Khan
Subjects
dc:subject × 8Rights
- Language dc:language
- English
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://csuepress.columbusstate.edu/theses_dissertations/233
- OAI identifier oai:identifier
- oai:csuepress.columbusstate.edu:theses_dissertations-1238