PROBABILITY DENSITIES FOR MILITARY SIMULATION AI: FUNCTIONALITY AND IMPACT IN FOG OF WAR SCENARIOS
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Authors
Jung, Hyokwon
Subjects
reinforcement learning
RL
fog of war
artificial intelligence
AI
computer science
cognitive AI
Atlatl
hexagon war
modeling
simulation
MOVES
fog
probability
density
probability density function
PDF
anti-submarine warfare
ASW
search and rescue
SAR
course of action
COA
RL
fog of war
artificial intelligence
AI
computer science
cognitive AI
Atlatl
hexagon war
modeling
simulation
MOVES
fog
probability
density
probability density function
anti-submarine warfare
ASW
search and rescue
SAR
course of action
COA
Advisors
Darken, Christian J.
Date of Issue
2024-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Despite significant technological advancements, the fog of war—uncertainty and incomplete information on the battlefield—continues to challenge military operations. Effective decision-making under such conditions remains a critical issue due to the lack of quantitative support tools. This thesis addresses this gap by incorporating military artificial intelligence (AI) into the hexagonal battlefield simulation environment known as the Atlatl platform, developed at Naval Postgraduate School. The research focuses on the development and evaluation of various AI algorithms, including scripted AIs, hierarchical and non-hierarchical, and reinforcement learning (RL) models. These models utilize probability distributions to enhance navigation and strategic planning under scenarios of the fog of war. By simulating numerous combat iterations, AI models demonstrate marked superiority in the precision and operational efficiency of locating and tracking enemy positions within the fog of war, which can aid commanders in decision-making. Furthermore, the insights gained from this research not only contribute to refining course of action (COA) decision-making in fog-of-war scenarios but also have practical applications in anti-submarine warfare (ASW) and maritime search and rescue (SAR) operations. This thesis highlights the effectiveness of employing AI with probability distributions to support decision-making.
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Thesis
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Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
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