Science Autonomy using Machine Learning for Astrobiology
This work aims to provide practical recommendations for improving autonomy in astrobiology missions, though it appears incremental as it focuses on existing challenges without introducing new methods.
The paper addresses the challenge of integrating AI and machine learning for autonomy in astrobiology space missions to distinguish biotic from abiotic patterns, but does not report specific results or numbers.
In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds. Advancing the integration of autonomy through AI and ML into space missions is a complex challenge, and we believe that by focusing on key areas, we can make significant progress and offer practical recommendations for tackling these obstacles.