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). In this paper, we developed a stand-alone set of pseudo data that mimicked the actual, classified data so that experimental excursions could be performed safely. We show LAILOW produces a score from a wargame like scenario for every available ship that might be moved. The score for each ship increases as fewer resources, e.g., lower cost, are required to fulfill an SLD plan requirement to move that ship to a new homeport. This produces a mathematical model that enables the immediate comparison between competing or alternate ship movement scenarios that might be chosen instead. We envision a more integrated, coherent, and large-scale, deep analytics effort leveraging methods that link to existing real data sources to more easily enable the direct comparisons of potential scenarios of platform movement considered through the SLD process. The resulting product could the facilitate decision makers’ ability to learn, document, and track the reasons for complex decision making of each SLD process and identify potential improvements and efficiencies for force development and force generation.