(SinBaD) Simulation in Behaviors and Decisions

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0337
Agency Tracking Number: N181-083-1127
Amount: $125,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N181-083
Solicitation Number: 2018.1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-04-05
Award End Date (Contract End Date): 2018-10-12
Small Business Information
1408 University Drive East, College Station, TX, 77840
DUNS: 555403328
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Timothy Darr
 (979) 260-5274
 tdarr@kbsi.com
Business Contact
 Jason Ogle
Phone: (979) 260-5274
Email: jogle@kbsi.com
Research Institution
N/A
Abstract
Knowledge-Based Systems Incorporated (KBSI) proposes Simulation in Behaviors and Decisions (SinBaD) in response to the Navy SBIR Warfighting Chess Games and Pieces (topic number N181-083). We will demonstrate proof-of-concept simulation components that have the capability to pit smart red/gray forces against smart blue forces to aid development of timely and effective decision support tools. This proposed capability will provide a service that is available for decision support tools to suggest modifications to current plans and predict future outcomes contextualized to a specific situation. Our demonstration scenario will be a lab-based 3-on-3 game in which both the red/gray commander and blue commander have three platoons with an objective of securing an area of interest. To increase our understanding, we will vary the parameters of the game to explore the differences in strategies. For example, the differences between an amphibious operation to secure a beachhead as opposed to one in a dense, urban environment to secure critical infrastructure. The Phase I results will demonstrate the feasibility of the approach for an operationally relevant toolset and decision support instrument. The Phase I results will also identify metrics to verify performance, and model normalization across simulation systems necessary to reduce technical risk.

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

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