Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces
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Authors
MacKinnon, Douglas J.
Zhao, Ying
Kendall, Walter Anthony
Schwamm, Riqui
Subjects
strategic laydown
posture
artificial intelligence
decision support
posture
artificial intelligence
decision support
Advisors
Date of Issue
2022
Date
2022
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
The SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.
Type
Report
Description
NPS NRP Executive Summary
Series/Report No
Department
Information Sciences (IS)
Information Sciences (IS)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
N3/N5 - Plans & Strategy
Funding
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
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.