Advancing Battlespace Data Prioritization for Mobile Command Posts with Context-Aware Reasoning and Adaptation

The ultimate goal of this effort is to provide the capability to dynamically—as a military operation unfolds—determine the level of prioritization for different pieces of battlespace data and information.   The Army is currently undertaking a major network modernization effort to transition from the big, largely static command posts that have been used for more than a decade to mobile commands posts that provide tactical agility in the face of current and future threats.  Part of this transition includes new support vehicles, command platforms, and integrated computing environment.  However, the modern battlespace is still characterized by the constraints and unpredictability of the tactical network, which will become even more critical under a dispersed mission command.     To address this gap, Stottler Henke proposes to develop APEX (Adaptable Prioritization EXchange), an intelligent data prioritization system that seeks to advance the state of the art in two related but separate ways: 1) automatically gathering factors that represent the current mission context; and 2) dynamically prioritizing information and adapting to changing information needs.  First, APEX will gather mission context factors from a variety of sources that include state-based factors (e.g., geographic location, network status), explicitly provided information (e.g., operator role, combat phase), and by inference or extraction from other sources of evidence (e.g., battlespace sensing, recent activity, running estimates).  These factors will directly define “cases” used to characterize various mission context states.  APEX will then employ a case-based reasoning approach to identify similar cases (i.e., mission contexts) that are each indirectly associated with a set of data priority rules.  The system will then adapt those associated prioritization rules and data management strategies to suit the current situation.  Lastly, this case-based approach endows APEX with the ability to add new cases to its case base over time by recognizing novel new mission context cases, by storing cases that have been combined/adapted to new situations, and by learning from manual soldier-specified changes to prioritization rules.  In this way, the system can continually learn new rules and the case base will essentially act as a record of the evolving state of battlespace data and information priorities.    Phase I research and prototype development will focus on identifying a wide range of mission context factors relevant to information prioritization while establishing the feasibility of our context-aware prioritization approach, which will be tailored to a handful of specific military scenarios.  This will lay the groundwork for continued development during Phase II by including additional mission context factors, implementing more sophisticated data prioritization strategies, and expanding the scope to a broader suite of scenarios.