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Framework for Intelligent Simulation Command with Hierarchically Embedded Reinforcement Learning (FISCHER)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0289
Agency Tracking Number: N181-083-0497
Amount: $999,828.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N181-083
Solicitation Number: 18.1
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-05-29
Award End Date (Contract End Date): 2021-06-01
Small Business Information
1400 Crystal Drive Suite 1400
Arlington, VA 22202
United States
DUNS: 036593457
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Rob Rua
 Machine Learning Scientist
 (703) 682-0758
 rob.rua@dac.us
Business Contact
 Becky Smith
Phone: (703) 682-1532
Email: becky.smith@dac.us
Research Institution
N/A
Abstract

Wargaming plays a critical role in the modern military, both as a decision support tool for command and control centers and as a training tool for developing the future force. In both of these applications it is valuable to have access to highly skilled automated actors. In a decision support context, this facilitates fast, high fidelity simulations with a variety of possible battlefield conditions. In a training context, this supports interactive learning tools and allows commanders to hone their skills through competition with difficult opponents.Historically, Artificial Intelligence agents that can match or beat expert human performance have been limited to turn-based games. However, due to the success of IBM’s Deep Blue system at Chess, and more recently, DeepMind’s AlphaGo system at Go, the latest research has begun to investigate if the techniques employed by these systems can be improved to achieve expert-level performance at less well-defined, real-time games.To manage the fundamental complexities that military-domain AI systems face, DECISIVE ANALYTICS Corporation proposes the Framework for Intelligent Simulation Command with Hierarchically Embedded Reinforcement Learning (FISCHER). FISCHER will leverage the latest developments in Game Theory and Reinforcement Learning to provide a wargaming artificial intelligence that can perform at a humanlike level.

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

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