Abstract

Virtual and Constructive (VC) simulations can be used to measure weapon effectiveness and support the capture of the operator's behavior. These areas are very non-linear, and Artificial Intelligence (AI) can support better analysis due to its way of dealing with incomplete information, noise, and nonlinearities. VC simulations focus on scenario variation and sensitivity analysis to find critical factors of success. This variation is a significant necessity for reducing bias. These scenario variations and the relative easiness to program numerous scenarios makes VC an excellent candidate to generate data from different sources. The combined data can be processed by an analytics tool, using AI/machine learning, to build useful models that can be used to study weapon effectiveness (and extrapolating beyond linear models) and build behavioral models that can encapsulate behavior. This encapsulated behavior can be used in other scenarios and study ways to enhance training or counterattacks. The dissertation ends with conclusions, limitations of the research project, contributions to the body of knowledge, and further research. The issues of new research go in details of potential methodologies using VC and AI/machine learning.

Notes

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Graduation Date

2020

Semester

Summer

Advisor

Rabelo, Luis

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008197; DP0023551

URL

https://purls.library.ucf.edu/go/DP0023551

Language

English

Release Date

August 2025

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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