ATLAS: Active Theory Learning for Automated Science
Researchers have developed ATLAS, an active learning framework designed to automate scientific discovery by generating and testing mechanistic hypotheses. This system, tested on cognitive science problems like recovering reinforcement learning agents, creates interpretable models and designs experiments to differentiate between them. ATLAS demonstrates a significant improvement in sample efficiency, outperforming random and even expert-designed experiments. AI
IMPACT Accelerates scientific discovery by automating hypothesis generation and experimental design for interpretable models.