Planning over MDPs through Probabilistic HTNs

In this paper, we propose a new approach to using probabilistic hierarchical task networks (HTNs) as an effective method for agents to plan under conditions in which their problem-solving knowledge is uncertain, and the environment is non-deterministic. In such situations it is natural to model the environment as a Markov Decision Process (MDP). We show that using Earley graphs, it is possible to bridge the gap between HTNs and MDPs. We prove that the size of the Earley graph created for any given HTN is bounded by the total number of tasks in the HTNs and show that from the Earley graph we can construct a plan for a given task that has the maximum expected utility when it is executed in an MDP environment.