ATLAS: Active Theory Learning for Automated Science

ATLAS: Active learning framework for automated discovery of interpretable behavioral models in cognitive science

ATLAS (Active Theory Learning for Automated Science) is a new active learning framework that iterates between generating mechanistic hypotheses as sparse neural network ensembles and designing maximally informative experiments to distinguish between them. The system is tested on recovering reinforcement learning agents from behavioral data in bandit tasks, achieving 5-10x sample efficiency improvements over random experimentation and matching expert-designed experiments from the literature. The work targets automated scientific discovery in cognitive science, with potential generalization to other domains requiring mechanistic modeling.