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MC-HAMMER: Mission Command - Human-centered Analysis of Machine learning Methods for Effectiveness and Resilience

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56KGU-18-C-0050
Agency Tracking Number: A181-037-0897
Amount: $99,994.50
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A18-037
Solicitation Number: 2018.1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-15
Award End Date (Contract End Date): 2019-02-14
Small Business Information
12 Gill Street
Woburn, MA 01801
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ms. Kara Orvis
 (703) 629-1823
Business Contact
 Mr. Thomas McKenna
Phone: (781) 496-2443
Research Institution

The Department of Defenses Third Offset Strategy is focused on ensuring military forces technological advantage over our adversaries. To achieve this goal, the DoD has identified five primary components, including a key need to leverage and integrate advanced algorithms and autonomous agents, capable of understanding their human counterparts in large systems. The Armys Common Operating Environment (COE) in general, and its Command Post Computing Environment (CP CE) in particular, seek to develop a consistent approach to integration and interoperability of applications and data, including those employing machine learning (ML). To enable an effective and resilient integration of ML technology into CP CE, Aptima and its partner, Apex, propose to conduct the Mission Command - Human-centered Analysis of Machine learning Methods for Effectiveness and Resilience (MC-HAMMER) study. MC-HAMMER will produce a principled method for assessing the applicability of ML to MC tasks and processes. This approach will rely on a coupled understanding of ML methods and MC cognitive work, and will be instantiated as a model of applicability that predicts costs, benefits, and risks of particular ML-MC pairings. This methodology will directly support decisions regarding how, when, and where to use ML algorithms and automated agents within MC systems and processes.

* Information listed above is at the time of submission. *

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