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Team Oriented Resource Management and Control (TORMAC)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: HR001122C0182
Agency Tracking Number: D2-2692
Amount: $1,481,240.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: HR001121S0007-25
Solicitation Number: HR001121S0007.T
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-07-22
Award End Date (Contract End Date): 2025-09-13
Small Business Information
3527 Beverly Glen Ter`
SHERMAN OAKS, CA 91423-4402
United States
DUNS: 124668711
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Paul Scerri
 (703) 342-4660
 pauls@percsolutions.com
Business Contact
 Gershon Weltman
Phone: (818) 788-1025
Email: gweltman@percsolutions.com
Research Institution
 Harvard University School of Engineering and Applied Science
 Miland Tambe
 
150 Western Ave.
Allston, MA 02134-1027
United States

 (617) 998-2423
 Nonprofit College or University
Abstract

  This proposal is to extend into Phase II for our development of Team Oriented Resource Management and Control (TORMAC), as a solution to the problem of distributed decision-making for resource constrained platforms in complex, adversarial environments. We are joined in this effort by Harvard University’s Center for Research on Computation and Society (CRCS), Teamcore Group. In Phase I we developed a scenario where rangers with UAVs and ground vehicles patrolled a wildlife reserve for poachers.  The rangers were supported by ground sensors listening for vehicles and drones-in-a-box placed strategically in the environment.  The objective of the rangers was to minimize poaching by finding poachers and tracking them when possible.  The adversarial poachers aimed to poach as many animals as possible, without being caught by the rangers.  This scenario features much of the complexity of more general problems, with spatial and temporal constraints on rangers, uncertainty about poachers, adversarial behaviors, and the need to react to stimulus. In Phase II our work will focus specifically on the game theoretic aspects of the cooperative reasoning.  There has been extensive study of adversarial bandit problems, where algorithms like Exp3 and its variants have proven excellent theoretical guarantees of low-regret learning against an adversary.

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

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