Research Summaries

Back Machine Learning Methods for Intelligence Collection from Strategic Targets

Fiscal Year 2019
Division Graduate School of Operational & Information Sciences
Department Operations Research
Investigator(s) Szechtman, Roberto
Sponsor Department of Defense Space (DoD)
Summary We will develop methods to efficiently collect intelligence from strategic targets in the absence of prior information. The resulting algorithms can be employed to geo-locate and characterize targets who attempt to avoid detection, and lies at the intersection of current machine learning and game theory research lines. Ultimately, this research will optimize the employment of intelligence collection assets in a budget constrained environment.
Keywords adversarial bandit perimeter surveillance
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal