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Army Research Laboratory Broad Agency Announcement for Basic and Applied Scientific Research -- W911NF-17-S-0003
The U.S. Army Research Laboratory (ARL) is the Department of the Army’s corporate laboratory and sole fundamental research laboratory. It is dedicated to scientific discovery, technological innovation, and the transition of knowledge products. ARL is situated within the U.S. Army Research, Development, and Engineering Command (RDECOM) – a U.S. Army Materiel Command (AMC) Major Subordinate Command (MSC). The ARL mission is to “Discover, innovate, and transition Science and Technology (S&T) to ensure dominant strategic land power”. To accomplish its mission, ARL executes fundamental research to address enduring S&T challenges identified by the Assistant Secretary of the Army for Acquisition, Logistics, and Technology [ASA(ALT)] and by priorities articulated by the Chief of Staff of the Army (CSA). In addition, the laboratory conducts research and analysis in emerging fields that may realize novel or vastly improved Army capabilities into the deep future (2030 and beyond).
The ARL BAA identifies topics of interest to the ARL Directorates (Computational and Information Sciences Directorate, Human Research and Engineering Directorate, Sensors and Electron Devices Directorate, Survivability/Lethality Analysis Directorate, Vehicle and Technology Directorate, and Weapons and Materials Research Directorate). The Directorates focus on executing in-house research programs, with a significant emphasis on collaborative research with other organizations in an Open Campus setting (Open Campus opportunities are described in detail at http://www.arl.army.mil/www/default.cfm?page=2357). The Directorates fund a modest amount of extramural research in certain specific areas, and those areas are described in this BAA.
The ARL BAA seeks proposals from institutions of higher education, nonprofit organizations, state and local governments, foreign organizations, foreign public entities, and for-profit organizations (i.e. large and small businesses) for research based on the following S&T campaigns: Computational Sciences, Materials Research, Sciences for Maneuver, Information Sciences, Sciences for Lethality and Protection, Human Sciences, and Assessment and Analysis. Further details are described in the ARL Technical Strategy and in the ARL S&T Campaigns located at www.arl.army.mil. These documents are subject to periodic refinements which may result in taxonomy inconsistencies. These inconsistencies should not affect the efficacy of the BAA to present a complete portfolio of essential ARL research.
Proposals are sought for cutting-edge innovative research that could produce discoveries with a significant impact to enable new and improved Army technologies and related operational capabilities and related technologies. The specific research areas and topics of interest described in this document should be viewed as suggestive, rather than limiting.
This BAA is a continuously open announcement valid throughout the period from the date of issuance through 31 March 2022, unless announced otherwise
Areas of Interest
Deadline: June 25, 2021
The aim of this Special Notice under the Army Research Laboratory (ARL) Broad Agency Announcement (BAA) (W911NF‐17‐S‐0003), under Grants.gov Opportunity W911NF‐17‐S‐0003‐SN‐MACHINE‐LEARNING, is to solicit research proposals related to the ARL BAA Topics, “KCI‐HS‐1: Robust Human and Machine Hybridization” and “CCE‐HS‐4: Humans in Multi‐Agent Systems.” The Army is interested in collaborating on research efforts to further develop the field of human‐guided machine learning in the following areas: novel forms of human‐intelligent technology decision making; cybernetics; hybridized thinking between man and intelligent technology; human technological savvy, and human‐intelligent technology teaming assessment tools.
The research goals in human‐guided machine learning are to integrate empirical and theoretical efforts and generating novel concepts and approaches for humans to influence and guide the evolving behavior of intelligent technologies with the goal of effectively solving complex problems under variable resource and time constraints. We generally characterize the complex problems as more ambiguously structured by having uncertain boundaries(if any) acrosstime and space,requiring massive and perhaps unattainable amounts of data in order to obtain complete certainty, and thus are computationally intractable for common analytic solutions. These problems may not have singularly optimal solutions, because problems will often have multiple, competing criteria and all solutions will ultimately reflect trade‐offs and reduction of optimality in meeting other criteria in the set.