Probabilistic hierarchical planning over MDPs
Yuqing Tang, Felipe Rech Meneguzzi, Katia P. Sycara, Simon Parsons · 2011
In this paper, we propose a new approach to using probabilistic hierarchical task networks (HTNs) as an effective method for agents to plan in 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 given HTNs is bounded by the total number of tasks in the HTNs and show that from the Earley graph we can then construct a plan for a given task that has the maximum expected value when it is executed in an MDP environment.