You are here

Human-Machine Teaming with Machine Learning Algorithms

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240520C0003
Agency Tracking Number: S2-0519
Amount: $899,892.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: SOCOM18B-001
Solicitation Number: 18.B
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-12-18
Award End Date (Contract End Date): 2021-06-18
Small Business Information
1712 Route 9 Suite 300
Clifton Park, NY 12065
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Arslan Basharat
 Assistant Director of Computer Vision
 (518) 881-4906
 arslan.basharat@kitware.com
Business Contact
 Wayne Durr
Phone: (518) 881-4925
Email: proposals@kitware.com
Research Institution
 University of Dayton Research Institute
 Wayne Durr Wayne Durr
 
300 College Park
Dayton, OH 45469
United States

 (518) 881-4925
 Nonprofit college or university
Abstract

Characterizing and understanding the interactions between human and machine plays an important role in extracting the most out of our machine learning algorithms while reducing human workload. We propose to develop a software prototype system that reduces user workload of exploiting AI algorithms for imagery exploitation. We will design a user-friendly system for content matching with interactive query refinement (IQR) and conduct human-machine teaming (HMT) user studies to inform the user interface and algorithm design choices. On Phase I. we have produced protocols for the HMT user studies based on reducing user workload through IQR. The feasibility study showed that the proposed novel approach of using active learning with IQR greatly reduces the user workload in searching FMV archives. We propose to develop an enhanced prototype based on such a design and further add novel techniques to improve product quality with reduced user workload. We propose to use techniques like knowledge distillation to avoid retraining deep learning algorithms. In order to establish user trust in AI, new cluster-based explanations with faster saliency map will be developed. The proposed technology should also generalize to other data sources like mid and high-altitude collection platforms and publicly available information.

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

US Flag An Official Website of the United States Government