OpportunityTitle: Active Transfer Learning (ATL)
AdditionalInformationOnEligibility: All responsible sources from academia and industry may submit proposals under this BAA.  Historically Black Colleges and Universities (HBCUs) and Minority Institutions (MIs) are  encouraged to submit proposals and join others in submitting proposals. However, no portion of  this BAA will be set aside for HBCU and MI participation.    Federally Funded research &amp; Development Centers (FFRDCs), including Department of Energy  National Laboratories, are not eligible to receive awards under this BAA. However, teaming  arrangements between FFRDCs and eligible principal bidders are allowed so long as they are  permitted under the sponsoring agreement between the Government and the specific FFRDC.  Navy laboratories and warfare centers as well as other Department of Defense and civilian  agency laboratories are also not eligible to receive awards under this BAA and should not  directly submit either white papers or full proposals in response to this BAA. If any such  organization is interested in one or more of the programs described herein, they should contact  the technical point of contact in Section I.7. As with FFRDCs, these types of federal  organizations may team with other responsible sources from academia and industry that are  submitting proposals under this BAA.  Some topics cover export controlled technologies. Research in these areas is limited to &#147;U.S. persons&#148; as defined in the International Traffic in Arms Regulation (ITAR) &#150; 22 CFR &#167; 1201.1 et seq. (See Section VII, Other Information)
Description: The overall objective of this program is to conduct basic research that will help enable robust autonomy and automation in dynamic, unconstrained environments and contexts. The two science problems of interest are how a learning machine may leverage all relevant prior knowledge and how it may leverage occasional in situ availability of a subject matter expert (SME). This leads naturally to the existing research of Transfer Learning and Active Learning. The intent of this Program is to improve upon these two existing areas of research and combine them to produce a novel, powerful learning capability. The primary deficiency of active learning is that it typically involves only labeling exemplars and does not allow the SME to fully impart his/her rich domain knowledge as they would to a human student. A deficiency of transfer learning is that when it fails it is typically not possible for an SME to repair or complete the transfer in situ. By exploiting both of these deficiencies, this Program seeks to fundamentally extend the scope of active learning and incorporate it into the knowledge transfer process. The two specific technical goals are to capitalize on the occasional availability of an SME to enable 1) the robust transfer of knowledge from existing sources and 2) the injection of new knowledge in situ. This first technical goal includes both machine-initiated and human-guided exploration of existing knowledge sources as well as machine-based reasoning on knowledge sufficiency for prompting SME queries. This second technical goal includes both machine-initiated queries of target knowledge as well as SME injection of new, rich domain knowledge into the target.
GrantorContactEmailDescription: Grants.Gov Questions Only
GrantorContactText: Jennifer Brown &lt;br/&gt;Contract Specialist &lt;br/&gt;Phone 703-588-2432
CloseDateExplanation: White Papers are due 06/28/2010 at 2:00 PM Local Eastern Time.  Full proposals are due 9/4/2010 at 2:00 PM Local Eastern Time.