Leveraging AI to Learn, Optimize, and Wargame Strategic Laydown and Dispersal of USN Operating Forces (Continuation)
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Authors
Zhao, Ying
MacKinnon, Douglas
Subjects
SLD
strategic laydown and dispersal
large language models
LLMs
force generation
force development
electronic models
standardization
lexical link analysis
LLA
strategic laydown and dispersal
large language models
LLMs
force generation
force development
electronic models
standardization
lexical link analysis
LLA
Advisors
Date of Issue
2025-03-28
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The SECNAV, based on recommendations from the CNO, disperses units of the Navy’s operating forces to locations in a deliberate manner that directly supports Department of Defense (DoD) guidance and policy. The current strategic, laydown, and dispersal (SLD) process is a manual, labor-intensive process and does not easily evaluate competing, alternative plans. The advanced artificial intelligence/machine learning (AI/ML) tools are studied to digitize, standardize, and automate many components of the current SLD process. In this continuous Phase III project, the NPS team continued, developed, and designed a research prototype with the integrated databases and AI/ML tools in the Naval Postgraduate School NIPRnet environment or the online secret-level research prototype (OSRP). Our achievements also include implementation of an AI Retrieval Augmented Generation (RAG) pipeline that integrates structured data recommendations with unstructured data with large language models (LLMs) for interpretation and justification of SLD decision making. The resulting OSRP can be used continuously not only as a platform and repository to accumulate and analyze historical SLD human decision data, documents, and knowledge, but perhaps most importantly, provide recommendations for future SLD decisions.
Type
Technical Report
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
NPS-IS-25-009
Sponsors
OPNAV N52
Naval Research Program
Naval Research Program
Funding
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
27 p.
Citation
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.