ACTIVE INFERENCE

Active Inference (ActInf) is a neuroscience inspired framework that is originally used to explain how biological agents learn and act in dynamic environments. It is based on minimizing a free energy bound on Bayesian surprise.  Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. We are exploring connections of ActInf to standard control and extend ActInf theory in multiple directions, such as chance constraints. 

Publications:

T. van de Laar, A. Özçelikkale, H. Wymeersch , Application of the Free Energy Principle to Estimation and Control, IEEE Trans. on Signal Processing, 2021 

T. van de Laar, I. Senoz, Ayça Özçelikkale, H. Wymeersch, Chance-Constrained Active Inference, IEEE Neural Computation, 2021