I see Oudeyer mentioned, nice. There is definitely other research that has to be mentioned in this field. Most prominently Karl Friston's work on free energy.

I see Oudeyer mentioned, nice. There is definitely other research that has to be mentioned in this field. Most prominently Karl Friston's work on free energy. It's a proper Bayesian formulation of optimizing for surprise, in this case minimization. The challenge is randomness in actions or environment. In a deterministic environment it might make sense to maximize surprise, however in a stochastic environment it makes sense to minimize it. You don't want a robot that navigates to those parts of space that are totally chaotic. Surprisingly, by minimizing surprise an agent can develop knowledge of the world on the long term as long as that world is dynamic enough. There are no dark rooms to retreat. Ralf Der argues for something similar from a dynamical perspective: https://www.informatik.uni-leipzig.de/~der/ https://www.informatik.uni-leipzig.de/~der/. It's about minimization towards (a set of) equilibrium states.