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Completed TRL 3 (started at 2, targeting 3)
Proposal evaluation of pre-Phase A mission concepts remains to be a human-dominated effort requiring the expertise of subject matter experts (SMEs) to determine whether or not candidate proposals hold merit in achieving the goals set out by the field of study to which the Request for Proposals (RFP) belongs. However, given the entirely human effort involved with evaluating and adjudicating a submission pool of proposals, challenges in evaluation consistency and agility remain as proposals are typically in the form of unstructured documentation. With somewhat recent advancements in computation ability, coupled with the rise of artificial intelligence and machine learning in all practices of science and engineering, there exists a notion of opportunity in applying semantic-based, computer-powered approaches in supporting the proposal evaluation process. This research proposes a natural language processing (NLP)-based proposal evaluation tool that may augment the reviewer’s capability in determining the utility of an overall portfolio of proposals in achieving the objectives set out by the scientific community. For context, and evaluation of efficacy, the results of the Astrophysics 2010 Decadal Survey will be used as the primary point of comparison.
This research will develop an ontology generation tool to enable engineers to make better informed decisions on their design space and amplify the already powerful modeling capabilities created by concurrent engineering frameworks.
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