Before revision: Surprise-based learning allows agents to adapt quickly in non-stationary stochastic environments . Most existing approaches to surprise-based learning and change point detection assume either implicitly or explicitly a simple, hierarchical generative model of observation sequences that are characterized by stationary periods separated by sudden changes.
After revision: Surprise-based learning allows agents to adapt quickly in non-stationary stochastic environments characterized by stationary periods separated by sudden changes.