Planning and navigation as active inference

Abstract

This paper introduces an active inference formulation of planning and navigation. It illustrates how the exploitation-exploration dilemma is dissolved by acting to minimise uncertainty (i.e., expected surprise or free energy). We use simulations of a maze problem to illustrate how agents can solve quite complicated problems using context sensitive prior preferences to form subgoals. Our focus is on how epistemic behaviour - driven by novelty and the imperative to reduce uncertainty about the world - contextualises pragmatic or goal-directed behaviour. Using simulations, we illustrate the underlying process theory with synthetic behavioural and electrophysiological responses during exploration of a maze and subsequent navigation to a target location. An interesting phenomenon that emerged from the simulations was a putative distinction between place cells - that fire when a subgoal is reached - and path cells - that fire until a subgoal is reached.