Publication:
Minitrack Introduction: Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response

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Authors
Bordetsky, Alex
Mullins, Steven J.
Hudgens, Bryan J.
Subjects
Advisors
Date of Issue
2021
Date
2021
Publisher
HICSS
Language
Abstract
Defense and emergency first responders must make rapid, consequential decisions and machine learning can aid analytics to support these decisions. Machine learning offers enormous promise, yet well publicized struggles reveal the need for better datasets and for opportunities to learn in challenging settings. Field experimentation offers the potential to meet these needs through iterative interactions in complex scenarios. Field experimentation can provide live action to facilitate high fidelity datasets that can support machine learning and artificial/augmented intelligence applications. These experiments may incorporate participants from academia; government agencies; militaries; first responders at all levels; and global industry partners. This minitrack explores the interplay between machine learning, field experimentation, and optimization analytics, whether exploratory, theoretical, experimental, in such critical areas as Defense and Emergency Response.
Type
Conference paper
Description
Proceedings of the 54th Hawaii International Conference on System Sciences 2021
The article of record as published may be found at http://http://hdl.handle.net/10125/70746
Series/Report No
Department
Other Units
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
1 p.
Citation
Bordetsky, Alex, Steve Mullins, and Bryan Hudgens. "Minitrack Introduction: Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response." HICSS, 2021.
Distribution Statement
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.
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