This chapter is about planning with hierarchical refinement methods. A plan guides the acting engine RAE with informed choices about the best methods for the task and context at hand. We consider an optimizing planner to find methods maximizing a utility function. In principle, the planner may rely on an exact dynamic programming optimization procedure. An approximation approach is more adapted to the online guidance of an actor. We describe a Monte Carlo tree search planner, called UPOM, parameterized for rollout depth and number of rollouts. It relies on a heuristic function for estimating the remainder of a rollout when the depth is bounded. UPOM is an anytime planner used in a receding horizon manner. This chapter relies on chapters 8, 9, and 14. It presents refinement planning domains and outlines the approach. Section 15.2 proposes utility functions and an optimization procedure. The planner is developed in Section 15.3.