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Smart Information Flow Technologies, LLC SBIR Phase II Award, January 2018

A SBIR Phase II contract was awarded to Smart Information Flow Technologies, LLC in January, 2018 for $1,480,434.0 USD from the U.S. Department of Defense and DARPA.

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Contents

sbir.gov/node/1592557
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
Smart Information Flow Technologies, LLC
Smart Information Flow Technologies, LLC
0
Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
DARPA
DARPA
0
Award Type
SBIR0
Contract Number (US Government)
W31P4Q-18-C-00340
Award Phase
Phase II0
Award Amount (USD)
1,480,4340
Date Awarded
January 22, 2018
0
End Date
April 22, 2020
0
Abstract

SIFT, along with Drs. Julie Adams and Ramesh Sagili of Oregon State University (OSU), propose to develop “Resilient Emergent Properties for Autonomous Agent InteRactions” (REPAIR). Building upon recent innovative work in swarm control and optimization, and applying new observations of specific defensive and adaptive characteristics of honeybee colonies, REPAIR will provide biologically inspired metaheuristic control algorithms for autonomous agents, along with a framework for ongoing development and evaluation. Recent honeybee research has identified two remarkable phenomena: altruism, where individual bees under duress remove themselves from the colony in order to protect the hive, and drifting, where a bee who has separated from its original colony is gradually adopted into a new one after a courting process. The primary objective of REPAIR will be to capture, in theoretical and algorithmic form, the unique behaviors of altruism and drifting, and to demonstrate the beneficial effects of these capabilities in simulated scenarios of interacting autonomous agents, both in the robotic and cyber domains. A Phase I pilot study at the OSU honeybee lab will observe the process of altruistic self-removal under duress, and the open-source Robotarium platform will be used for preliminary evaluation of new metaheuristic control policies in the REPAIR framework.

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