Leverage Artificial Intelligence to Learn, Optimize, and Wargame (LAILOW) for Navy Ships

Navy ships are complex enterprises comprised of multiple organizations that must interact smoothly and interface externally without threats to efficiency and combat-readiness. As logistical challenges increase and technology pushes response times, it is critical to introduce state of the art computational methods for analyzing the interlocked systems and training for different events. To address these challenges in this context, we introduce a framework called LAILOW: learn, optimize, and wargame. LAILOW exploits data arising from multiple sources in a complex enterprise by offering data mining, machine learning, and predictive algorithms that can be used for analysis and discovery of patterns, rules, and anomalies. LAILOW's output can then be used to optimize business processes and course of actions. We show three use cases of using the of LAILOW framework. We show the whole LAILOW framework to search for vulnerability of a major Marine equipment's maintenance and supply system for difficult tests and evolve resilience and novel solutions accordingly. We show using of lexical link analysis (LLA) as part of LAILOW to improve the prediction accuracy of probability of failure of critical Navy Ship parts, related to C4I systems, for NAVWARSYSCOM's Predictive Risk Sparing Matrix (PRiSM) product. We also show the comparison of LLA prioritizing items in the Financially Restricted Work Que (FRWQ) with the baseline calculation.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited