Probabilistic inference as a model of planned behavior

The problem of planning and goal-directed behavior has been addressed in computer science for many years, typically based on classical concepts like Bellman’s optimality principle, dynamic programming, or Reinforcement Learning methods – but is this the only way to address the problem? Recently there is growing interest in using probabilistic inference methods for decision making and planning. Promising about such approaches is that they naturally extend to distributed state representations and efficiently cope with uncertainty. In sensor processing, inference methods typically compute a posterior over state conditioned on observations – applied in the context of action selection they compute a posterior over actions conditioned on goals. In this paper we will first introduce the idea of using inference for reasoning about actions on an intuitive level, drawing connections to the idea of internal simulation. We then survey previous and own work using the new approach to address (partially observable) Markov Decision Processes and stochastic optimal control problems.