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Probabilistic Reasoning for Enhanced Course of Action Generation (PRECOG)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-15-M-0501
Agency Tracking Number: F151-096-0147
Amount: $149,916.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF151-096
Solicitation Number: 2015.1
Timeline
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-07-29
Award End Date (Contract End Date): 2016-04-29
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138-4555
United States
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Brian Ruttenberg
 Scientist
 (617) 491-3474
 bruttenberg@cra.com
Business Contact
 Mark Felix
Phone: (617) 491-3474
Email: contracts@cra.com
Research Institution
N/A
Abstract

ABSTRACT:Autonomous operations in space, particularly with respect to health and resilience, are becoming increasingly important features of satellite platforms. Systems that respond to threat indicators from sensor fusion and external sources have to generate and evaluate potentially large sets of COAs based on incomplete or noisy data. Such systems need to reason under uncertainty and anticipate possible contingencies for plans that involve complex sequencing and sensor tasking. The Probabilistic Reasoning for Enhanced Course of Action Generation (PRECOG) system uses advanced probabilistic reasoning methods to represent complex COAs and their evolution under uncertainty and to optimize the choice of COAs against multiple objectives. The representation of COAs includes the ability to task sensors and allows execution of actions to depend on the occurrence of contingent events or the results of sensor fusion. PRECOG will advance the state of the art in decision making algorithms, enabling efficient COA selection on board a satellite.BENEFIT:Satellite autonomy, which enables satellites to take action in response to threats without having to wait for ground control, is a growing field. PRECOG will support satellite autonomy by efficiently selecting an optimal COA in response to a situation, while reasoning about and anticipating uncertainties and contingencies. In addition, PRECOG is highly relevant to unmanned autonomous vehicles in general, all of which could benefit from the capability to rapidly respond to situations without needing human control.

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

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