  The Free Energy Principle (FEP) describes (biological) agents as minimising a
variational Free Energy (FE) with respect to a generative model of their
environment. Active Inference (AIF) is a corollary of the FEP that describes
how agents explore and exploit their environment by minimising an expected FE
objective. In two related papers, we describe a scalable, epistemic approach to
synthetic AIF agents, by message passing on free-form Forney-style Factor
Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG)
notation that visually represents (generalised) FE objectives for AIF. The
current paper (part II) derives message passing algorithms that minimise
(generalised) FE objectives on a CFFG by variational calculus. A comparison
between simulated Bethe and generalised FE agents illustrates how synthetic AIF
induces epistemic behaviour on a T-maze navigation task. With a full message
passing account of synthetic AIF agents, it becomes possible to derive and
reuse message updates across models and move closer to industrial applications
of synthetic AIF.
