Leverage AI To Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the Operating Forces of The U.S. Navy
Abstract
The Secretary of the Navy disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current strategic laydown and dispersal (SLD) process is labor intensive, time intensive, and less capable of becoming agile for considering competing alternative plans. SLD could benefit from the implementation of artificial intelligence. We introduced a relatively new methodology to address these questions which was recently derived from an earlier Office of Naval Research funded project that combined deep analytics of machine learning, optimization, and wargames. This methodology is entitled LAILOW which encompasses Leverage AI to Learn, Optimize, and Wargame (LAILOW). We began by collecting data then employed data mining, machine learning, and predictive algorithms to perform artificial intelligent analysis to learn about and understand the data. This data included historical, phased force deployment data among others to learn patterns of what decisions were made and how they were executed. We then developed a stand-alone set of pseudo data that mimicked the actual, classified data so that experimental excursions cold be performed safely. We also limited our data to include ships. Our efforts produced a first-ever, relative, and optimal, score derived from a wargame like scenario for every available ship that might be moved. The score for each ship increases as fewer resources are required to fulfill an SLD plan requirement to move that ship to a new homeport. This not only produced a mathematically optimal response, but also enabled the immediate comparison between competing or alternate ship movement scenarios that might be chosen instead.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 06, 2022
- Accession Number
- AD1189483
Entities
People
- Douglas J. MacKinnon
- Ying Zhao
Organizations
- Naval Postgraduate School