Intelligence and Intuition for Enhanced Decision Making (I2EDM)

Award Information
Agency:
Department of Defense
Amount:
$80,000.00
Program:
STTR
Contract:
N00014-13-P-1178
Solitcitation Year:
2013
Solicitation Number:
2013.A
Branch:
Navy
Award Year:
2013
Phase:
Phase I
Agency Tracking Number:
N13A-024-0140
Solicitation Topic Code:
N13A-T024
Small Business Information
Modus Operandi, Inc.
709 South Harbor City Blvd., Suite 400, Melbourne, FL, -
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
130550262
Principal Investigator
 Teresa Nieten
 Scientist
 (321) 473-1426
 tnieten@modusoperandi.com
Business Contact
 Peter Dyson
Title: CEO
Phone: (321) 473-1444
Email: pdyson@modusoperandi.com
Research Institution
 Institute for Human&Machine Cogni
 Diana Thacker
 40 S. Alcaniz St
Pensacola, FL, 32502-
 (850) 202-4473
 Domestic nonprofit research organization
Abstract
The focus of our Intelligence and Intuition for Enhanced Decision Making (I2EDM) Phase 1 research is to provide efficient and timely automated production and dissemination of information products in support of doctrinal Decision Points for the Company and below in austere environments. Operating in the Cloud, I2EDM will continuously fuse tactical information with human intuition and experience to push data relevant to the decision support matrix to the Commander in the field. The Modus Operandi Team's solution will define, prototype, and develop a fusion approach that goes beyond JDL level 0 (Subobject assessment) and 1 (Object assessment) fusion to approach level 2 (Situation assessment) and level 3 (Impact assessment) fusion. The intelligence data will continue to be updated in the cloud as new intelligence arrives, even when the end users are offline. As processing is completed, the distributed data will then be recombined, or reduced, into the normalized fused results, and made available to the decision makers or analysts for further refinement. The analyst will then have the opportunity to adjust certainty assessments and constraining assumptions, add missing information, remove irrelevant or inaccurate data, and otherwise influence the direction of machine processing prior to the predictive analysis step.

* information listed above is at the time of submission.

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