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Design, Reconfigure, and Evaluate Autonomous Models in Training

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-15-M-6662
Agency Tracking Number: F15A-T14-0078
Amount: $149,998.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF15-AT14
Solicitation Number: 2015.1
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-08-18
Award End Date (Contract End Date): 2016-05-16
Small Business Information
100 E. Rivercenter Blvd Suite 100
Covington, KY 41011
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Walter Warwick
 Principal Scientist
 (859) 663-2114
Business Contact
 Stuart M Rodgers
Phone: (859) 663-2114
Research Institution
 Carnegie Mellon University
 Carnegie Mellon University
5000 Forbes Avenue BH 345A
Pittsburgh, PA 15213
United States

 (412) 268-6028
 Domestic Nonprofit Research Organization

ABSTRACT: The proliferation of autonomous- and human-machine systems necessitates the creation of new tools for system design and evaluation. Key among these are simulation testbeds that support interactions between multiple warfighters and systems, and methods for creating and integrating intelligent agent models into simulation environments. We see a significant opportunity to advance the state of the art in human-machine systems, including the efficiency of developing intelligent agent models, the integration of agent representations into simulation environments, and the speed of evaluating human-machine systems using simulation environments. We refer to our solution as DREAMIT Design, Reconfigure and Evaluate Autonomous Models In Training. DREAMIT centers on two concepts. The first is to combine multiple intelligent agent models, specified at different levels of abstraction, within the simulation environment. By leveraging different levels of abstraction, agent models will produce behavior at the desired levels of fidelity without incurring undue computational complexity and cost. The second concept is to create a virtual data loop. The aim is to capture data from training simulations, to apply computational methods to the data in order to facilitate agent development, and to close the loop by gathering additional data after re-embedding agent models in the simulation environment.; BENEFIT: In creating DREAMIT, we expect to deliver a best-in-class solution to satisfy the Air Forces needs to conduct multi-agent simulations, to accelerate the pace of developing intelligent agent models, and to evaluate human-machine systems more efficiently. The major benefits of DREAMIT are: (1) reconfigurability, in terms of the exercise scenario, and the constituent live and simulated players; (2) adaptability of intelligent agent models based on training history and encountered tactics; (3) anticipation of Red and Blue Force responses to new tactics based on intelligent agent models; and (4) efficient and economical evaluation of alternate human-machine system designs. These features will make DREAMIT applicable in other domains, military and commercial alike, where flexible scenario construction and adversary prediction are needed for training; for example, in other military services, for emergency responders, and in security response coordination.

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

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