Please use this identifier to cite or link to this item: https://dair.nps.edu/handle/123456789/4396
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYing Zhao, Gabe Mata-
dc.contributor.authorErik Hemberg, Una May O'Reillu-
dc.contributor.authorNate Derbinsky, Bruce Cormany-
dc.contributor.authorJoy Allen, Andrew Haley-
dc.contributor.authorAdam Hilliard-
dc.date.accessioned2021-05-18T20:16:02Z-
dc.date.available2021-05-18T20:16:02Z-
dc.date.issued2021-05-10-
dc.identifier.citationPublished--Unlimited Distributionen_US
dc.identifier.urihttps://dair.nps.edu/handle/123456789/4396-
dc.descriptionAcquisition Management / Defense Acquisition Community Contributoren_US
dc.description.abstractNavy 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.en_US
dc.description.sponsorshipAcquisition Research Programen_US
dc.language.isoen_USen_US
dc.publisherAcquisition Research Programen_US
dc.relation.ispartofseriesAcquisition Management;SYM-AM-21-089-
dc.subjectArtificial Intelligenceen_US
dc.subjectLAILOWen_US
dc.subjectNavy Shipsen_US
dc.titleLeverage Artificial Intelligence to Learn, Optimize, and Wargame (LAILOW) for Navy Shipsen_US
dc.typeBook chapteren_US
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

Files in This Item:
File Description SizeFormat 
SYM-AM-21-089.pdf1.57 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.