  Previous work on planning as active inference addresses finite horizon
problems and solutions valid for online planning. We propose solving the
general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as
probabilistic inference. Furthermore, we discuss online and offline methods for
planning under uncertainty. In an SSP MDP, the horizon is indefinite and
unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are
widely used in the artificial intelligence community. Additionally, we
highlight some of the differences between solving an MDP using dynamic
programming approaches widely used in the artificial intelligence community and
approaches used in the active inference community.
