SBIR-STTR Award

Reinforcement Learning for Intelligent Salvo Management (RLISM)
Award last edited on: 9/8/22

Sponsored Program
SBIR
Awarding Agency
DOD : MDA
Total Award Amount
$149,994
Award Phase
1
Solicitation Topic Code
MDA21-021
Principal Investigator
Richard Wood

Company Information

Scientific Systems Company Inc (AKA: SSCI~Scientific Systems Inc)

500 West Cummings Park Suite 3000
Woburn, MA 01801
   (781) 933-5355
   info@ssci.com
   www.ssci.com
Location: Single
Congr. District: 05
County: Middlesex

Phase I

Contract Number: HQ0860-22-C-7076
Start Date: 12/6/21    Completed: 6/5/22
Phase I year
2022
Phase I Amount
$149,994
Reinforcement Learning for Intelligent Salvo Management (RLISM) is an automated Artificial Intelligence (AI)/Machine Learning (ML) driven battle management software decision aid for intercept planning of ballistic missiles across multiple salvos. The decision aid system provides functionality for an operator-configurable offline (pre-mission) Monte-Carlo simulation across huge numbers of scenario permutations, in order to train the reinforcement learning (RL) algorithm components. RLISM provides an operator-configurable simulation and user interface to model, test, assess, and train operators with the RLISM online capabilities. Incorporated within RLISM is a pre-launch constraint satisfaction combinatorial solver, and associated user interface, to optimize, plan, and assess interceptor salvo compositions, configurations, and schedule timing. To incorporate in-flight updates, a mission management capability, and associated user interface, is provided for reactive engagement re-tasking in real time. The algorithmic basis to accomplish the above will combine algorithms for combinatorial optimization and reinforcement learning, to leverage the “best of both worlds” – the deterministic constraint handling of combinatorial optimization, with the speed and flexibility of RL. Approved for Public Release | 21-MDA-11013 (19 No

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
Phase II year
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