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Innovative Uses of Artificial Intelligence and Machine Learning in Scenario Planning and Design

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-22-C-7022
Agency Tracking Number: B212-007-0057
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA21-007
Solicitation Number: 21.2
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2021-12-06
Award End Date (Contract End Date): 2022-06-05
Small Business Information
2241 17th Street
Boulder, CO 80302-1111
United States
DUNS: 128005423
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael Deskevich
 (303) 447-3255
Business Contact
 Jim Kelly
Phone: (303) 447-3255
Research Institution

A simulation optimization tool can exploit existing missile defense simulation models to streamline the scenario generation analysis process. OptTek's Phase I project will focus on using a simulation optimization approach to solve the problem of generating a minimal collection of test scenarios, using the minimum number of shared scenario components, that meets a required set of test objectives. Each individual scenario taken through the end to end scenario generation process involves substantial effort including discussion, documentation, and manual scenario generation in high fidelity tools. Likewise, scenario components require a great deal of time and effort to prepare for the test event. We will explore the use of an optimization tool with medium fidelity digital simulation models early in the scenario generation process to minimize the required test scenarios and scenario components. OptTek proposes to enhance previously created scenario generation optimizer software, updating it to work with the current software. Additionally, artificial intelligence (AI) /machine learning (ML) predictors will be created and trained to automatically suggest independent combinations of test objectives in scenarios that will be used to make the scenario generation process more efficient. Finally, to support digital test and assessment, mechanisms for adaptive management of digital tests will be explored that will reduce the number of simulation runs needed to achieve desired assessment results. Approved for Public Release | 21-MDA-11013 (19 Nov 21)

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

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