Search Results for author:
Found 6 papers, 0 papers with code
Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems
Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences.
Kalman filters as the steady-state solution of gradient descent on variational free energy
In this work, we present a straightforward derivation of Kalman filters consistent with active inference via a variational treatment of free energy minimisation in terms of gradient descent.
Scaling active inference
In reinforcement learning (RL), agents often operate in partially observed and uncertain environments.
Generative models as parsimonious descriptions of sensorimotor loops
The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world.
Nonmodular architectures of cognitive systems based on active inference
We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent.
A Minimal Active Inference Agent
Research on the so-called "free-energy principle'' (FEP) in cognitive neuroscience is becoming increasingly high-profile.
