Publication:
ENLISTING AI IN COURSE OF ACTION ANALYSIS AS APPLIED TO NAVAL FREEDOM OF NAVIGATION OPERATIONS

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Authors
Allen, John T., II
Subjects
AI
artificial intelligence
COA
course of action
COA development
USN
Navy
FONOPS
freedom of navigation
wargaming
RL
reinforcement learning
Advisors
Darken, Christian J.
Alt, Jonathan K.
Date of Issue
2022-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Navy Planning Process (NPP) Course of Action (COA) analysis requires time and subject matter experts (SMEs) to function properly. Independent steamers (lone destroyers) can soon find themselves lacking time or more than 1–2 SMEs or both. Artificial Intelligence (AI) techniques implemented in real-time strategy (RTS) wargames can be applied to military wargaming to aid military decision-makers’ COA analysis. Using a deep-Q network (DQN) and the ATLATL wargaming framework, I was able to train AI agents that could operate as the opposing force (OPFOR) commander at both satisfactory and near-optimal levels of performance, after less than 24 hours of training or 500000–learning steps. I also show that under 6 hours or 150000–learning steps does not result in a satisfactory AI admiral capable of playing the role as the OPFOR commander in a similarly sized freedom of navigation operation (FONOP) scenario. Applying these AI techniques can save both time onboard and time for reachback personnel. Training AI admirals as quality OPFOR commanders can enhance the NPP for the entire Navy without adding additional strain and without creating analysis paralysis. The meaningful insights and localized flashpoints revealed through hundreds of thousands of constructive operations and experienced by the crew in live simulation or simulation replays will lead to real world, combat-ready naval forces capable of deterring aggression and maintaining freedom of the seas.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
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Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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