active-inference

Gaussian approximations to nonlinear observation functions hat are sensitive to curvature, such as a second-order Taylor approximation, produce a state-dependent ambiguity term in expected free energy minimization.

Analysis of Bayesian Intelligent Autonomous Systems focuses on the information-theoretic basis of Active Inference. What are the best data points for an agents? How optimal is data collection based on minimizing expected free energy?

We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model.

Design of an intelligent autonomous system for quadrupedal robot locomotion using Active Inference (AIF). AIF is a neuroscience-based framework of perception and action, and could bring us closer to how animals learn to walk.

Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems. Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer.