CAT Learning Algorithm Workbench (CLAW)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00024-12-P-4032
Agency Tracking Number: N113-176-0016
Amount: $149,979.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: N113-176
Solicitation Number: 2011.3
Small Business Information
625 Mount Auburn Street, Cambridge, MA, -
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Wayne Thornton
 Senior Scientist
 (617) 491-3474
 wthornton@cra.com
Business Contact
 Mark Felix
Title: Contracts Manager
Phone: (617) 491-3474
Email: mfelix@cra.com
Research Institution
 Stub
Abstract
Current countermeasure anti-torpedo (CAT) systems use explicit logic to direct intercepts resulting in an inability to adapt to the complexities of the stochastic marine environment. The CAT Learning Algorithm Workbench (CLAW) is an analytical research testbed capable of comparing the effectiveness of different machine learning approaches to optimize and automate anti-torpedo fire control and develop criteria concepts for discriminating among them. By applying recent developments in intelligent algorithms to existing simulations and models in the program of record using an instrumented test environment, investigators can identify the most promising designs for using adaptive learning in the Torpedo Warning System. The benefit of the approach is to harden battle group defenses against torpedo salvos by finding optimal fire control solutions and automating the launch decision process.

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

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