Leveraging Machine Learning for Operation Assessment
Leveraging Machine Learning for Operation Assessment
Click to enlarge
Author(s): Adgie, Mary Kate
Brown, Ryan Andrew
Egel, Daniel
ISBN No.: 9781977404435
Pages: 96
Year: 202206
Format: Trade Paper
Price: $ 31.74
Dispatch delay: Dispatched between 7 to 15 days
Status: Available

The authors describe an approach for leveraging machine learning to support assessment of military operations. They demonstrate how machine learning can be used to rapidly and systematically extract assessment-relevant insights from unstructured text available in intelligence reporting, operational reporting, and traditional and social media. These data, already collected by operational-level headquarters, are often the best available source of information about the local population and enemy and partner forces but are rarely included in assessment because they are not structured in a way that is easily amenable to analysis. The machine learning approach described in this report helps overcome this challenge. The approach described in this report, which the authors illustrate using the recently concluded campaign against the Lords Resistance Army, enables assessment teams to provide commanders with near-real-time insights about a campaign that are objective and statistically relevant. This machine learning approach may be particularly beneficial in campaigns with limited or no assessment-specific data, common in campaigns with limited resources or in denied areas. This application of machine learning should be feasible for most assessment teams and can be implemented with publicly and freely available machine learning tools pre-authorized for use on U.S.


Department of Defense systems.


To be able to view the table of contents for this publication then please subscribe by clicking the button below...
To be able to view the full description for this publication then please subscribe by clicking the button below...