Abstract

Using Artificial Intelligence to Support Science Prioritization by the Decadal Surveys

Highlights

  • Background and MotivationThe current National Academies’ Decadal Survey on Planetary Science and Astrobiology is formally the third in the series of generally similar long-range prioritization activities intended to help the funding agencies identify the highest priority goals in planetary sciences for the subsequent 10+ years

  • We believe that there is currently sufficient justification for the National Academies to consider a potentially significant augmentation. We advocate that they supplement the established Survey process with identification of promising science priorities identified by application of Artificial Intelligence (AI) and Machine Learning (ML, a branch of AI) techniques

  • In this paper we summarize the case for using AI in this manner and give examples of modest inexpensive demonstration trials, including an AI assessment of the white papers submitted to this Decadal Survey

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Summary

Executive Summary

Science funding agencies (NASA, DOE, and NSF), the science communities, and the US taxpayer have all benefited enormously from the several-decade series of National Academies’ Decadal Surveys These Surveys are a primary means whereby these agencies manage and advocate strategic science and technology priorities and funding on behalf of the scientific communities. They comprise highly regarded subject matter experts whose goal is to develop a set of priorities that are recommended for major investments in the subsequent 10+ years. We advocate that they supplement the established Survey process with identification of promising science priorities identified by application of Artificial Intelligence (AI) and Machine Learning (ML, a branch of AI) techniques These techniques are already being successfully applied elsewhere in long-range planning and prioritization. We summarize our progress to date of our experimental “forecasting” the results of the Astro2010 Survey using AI as training for predicting the Astro2020 Survey results

Background and Motivation
Our Demonstration of AI-Supported Prioritization
Inputs
Outputs
Findings
Summary and Suggested Next Steps
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