Battle Management Aids: Leveraging Artificial Intelligence for Tactical Decisions
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Authors
Johnson, Bonnie W.
Green, John M.
Kendall, Walter
Miller, Scot A.
Godin, Arkady A.
Zhao, Ying
Subjects
artificial intelligence
machine learning
battle management aids
human-machine teaming
automated decision aids
weapons engagements
mission planning
human-machine trust
air and missile defense
cognitive laser
laser weapon system
machine learning
battle management aids
human-machine teaming
automated decision aids
weapons engagements
mission planning
human-machine trust
air and missile defense
cognitive laser
laser weapon system
Advisors
Date of Issue
2021
Date
2021
Publisher
Monterey, California: Naval Postgraduate School
Monterey, California. Naval Postgraduate School.
Monterey, California. Naval Postgraduate School.
Language
en_US
Abstract
This study will explore the needs and requirements for battle management aids that leverage artificial intelligence methods to enhance tactical decisions. The Navy has recognized the need for tactical decision aids to support battle management as warfighters become overwhelmed with shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. Tactical decision aids must provide a common operational picture to distributed commands that meets the diverse mission needs as well as bridges the gap between planning and tactical domains. The decision aids must support coordinated C4ISR, active and passive operations, offensive and defensive tactics, and information warfare. The study will develop a conceptual design of a decision aid and architecture based on artificial intelligence, machine learning, predictive analytics, and game theory that produces a common operational picture and recommended tactical courses of action based on predicted performance, outcomes, and effects. The primary objectives of this study will be to understand the diverse needs and requirements for battle management aids and to develop a conceptual design of a decision aid system and architecture based on artificial intelligence, machine learning, game theory, and predictive analytics.
Type
Report
Description
NPS NRP Executive Summary
Series/Report No
Department
Systems Engineering
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
N2/N6 - Information Warfare
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
Citation
Distribution Statement
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.