全球智库信息集成服务系统

Machine learning can be a powerful tool for supporting operation assessment

• Data already collected by operational-level headquarters — intelligence reporting, operational reporting, and ambient data (social and traditional media) — are often the best available types of information about the enemy and partner forces and the local population. Yet they are rarely integrated into assessment, because they are often (1) not perceived as sufficiently objective, (2) not available in a structured format easily amenable to analysis, and (3) extremely numerous and require some effort to obtain and organize.
• Machine learning (ML) tools, which can rapidly ingest and interpret large quantities of unstructured text, allow rapid, systematic, and objective analysis of these data, producing insights about the campaign that are objective and statistically relevant.
• Supervised machine learning (SML) is the simplest approach for using ML to incorporate these data into the assessment process. In the SML approach, the assessment team first analyzes, by hand, a subset of the unstructured text and then applies ML algorithms to mimic the assessment team's analytical approach for the remaining data.
• ML-derived data can provide a commander with near-real-time insights about a campaign, with each type of data (intelligence, operational, and ambient) providing a different lens for understanding a campaign's effects.
• ML tools are particularly beneficial in campaigns with limited or no assessment-specific data — which is common in campaigns with limited resources or in denied areas.
• This ML-based approach should be feasible for most assessment teams and can be implemented with freely available ML tools that are pre-authorized for use on U.S. Department of Defense classified systems.

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 Lord's 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.

• Chapter One

  Introduction

• Chapter Two

  Supervised Machine Learning and Assessment

• Chapter Three

  Operationalizing Available Data for Assessment

• Chapter Four

  Example Using Operation Observant Compass

• Chapter Five

  Recommendations and Next Steps

• Appendix A

  Support Vector Machine

• Appendix B

  Code for Example in Chapter Four