White papers are due on June 28, 2010 at 2:00 p.m. Eastern Time (ET). Full proposals are due on September 4, 2010 at 2:00 p.m. ET.
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.The distinct subject matter of this funding opportunity necessitates a thorough review of the official program guidance referenced at the URL provided in the contact section of this summary.
None is available.
Varies
Name: Jason Stack, Ph.D. Program Officer
Department: Office of Naval Research (ONR)
Street: One Liberty Center
875 N. Randolph Street
City: Arlington
Zip: 22203-1995
Email: jason.stack@navy.mil
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