  Research on the so-called "free-energy principle'' (FEP) in cognitive
neuroscience is becoming increasingly high-profile. To date, introductions to
this theory have proved difficult for many readers to follow, but it depends
mainly upon two relatively simple ideas: firstly that normative or teleological
values can be expressed as probability distributions (active inference), and
secondly that approximate Bayesian reasoning can be effectively performed by
gradient descent on model parameters (the free-energy principle). The notion of
active inference is of great interest for a number of disciplines including
cognitive science and artificial intelligence, as well as cognitive
neuroscience, and deserves to be more widely known.
  This paper attempts to provide an accessible introduction to active inference
and informational free-energy, for readers from a range of scientific
backgrounds. In this work introduce an agent-based model with an agent trying
to make predictions about its position in a one-dimensional discretized world
using methods from the FEP.
