On Learning Control Knowledge for a HTN-POP Hybrid Planner

In this paper we present a learning method that is able to automatically acquire control knowledge for a hybrid HTN-POP planner called Hybis. Hybis decomposes a problem in subproblems using either a default method or a user-defined decomposition method. Then, at each level of abstraction, generates a plan at that level using a POCL (Partial Order Causal Link) planning technique. Our learning approach builds on Hamlet, a system that learns control knowledge for a total order non-linear planner (Prodigy4.0). In this paper, we focus on the operator selection problem for the POP component of Hybis, which is very important for efficiency purposes.