Increasing Confidence in Machine Learned (ML) Functional Behavior during Artificial Intelligence (AI) Development using Training Data Set Measurements
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Authors
Nagy, Bruce
Subjects
Advisors
Date of Issue
2021-05-10
Date
05/10/21
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
The mission of the Office of the Under Secretary of Defense for Acquisition and Sustainment (OUSD A&S) is to quickly and cost effectively deliver and sustain secure and resilient capabilities to warfighters and international partners. There are urgent requirements to develop adaptive acquisition framework (AAF) to speed up software development and acquisition processes that strengthen the concepts of operations (CONOPS) such as distributed maritime operations (DMO). It is imperative for the Department of Defense (DoD) to shape the AAF using data-driven analysis linked to the National Defense Strategy and the nature of global threats, and scale new capabilities to counter new threats. The threat and capability coevolutionary matrix (TCCM) addresses the requirement. A threat is a problem a capability tries to deal with. A capability is the solution to the problem that represents a threat. Coevolutionary algorithms explore domains in which the quality of a capability or combination of capabilities is determined by its ability to successfully defeat a threat or combination of threats. TCCM has the potential to systematically optimize, recommend, and coevolve capabilities and threats in new and contested environments. We show a use case regarding helping a program executive office (PEO) to wargame capabilities and threats against a specific domain DMO using unclassified data compiled from open sources.
Type
Presentation
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
SYM-AM-21-086
Sponsors
Prepared for the Naval Postgraduate School, Monterey, CA 93943.
Naval Postgraduate School
Naval Postgraduate School
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
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.