On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference

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) probabilistic inference. Furthermore, we discuss offline methods under uncertainty. In an SSP MDP, is indefinite unknown a priori. MDPs generalize infinite are widely used in artificial intelligence community. Additionally, highlight some of differences between MDP using dynamic programming approaches community F