Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker

Active inference is a state-of-the-art framework in neuroscience that offers a unified theoryof brain function. It is also proposed as a framework for planning in AI. Unfortunately, thecomplex mathematics required to create new models — can impede application of activeinference in neuroscience and AI research. This paper addresses this problem by providinga complete mathematical treatment of the active inference framework — in discrete timeand state spaces — and the derivation of the update equations for any new model. Weleverage the theoretical connection between active inference and variational message passingas describe by John Winn and Christopher M. Bishop in 2005. Since, variational messagepassing is a well-defined methodology for deriving Bayesian belief update equations, thispaper opens the door to advanced generative models for active inference. We show thatusing a fully factorized variational distribution simplifies the expected free energy — that furnishes priors over policies — so that agents seek unambiguous states. Finally, we considerfuture extensions that support deep tree searches for sequential policy optimisation — basedupon structure learning and belief propagation.