  Hierarchical Task Network (HTN) planning usually requires a domain engineer
to provide manual input about how to decompose a planning problem. Even
HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer
to annotate the tasks with information about what to learn. We introduce
CURRICULAMA, an HTN method learning algorithm that completely automates the
learning process. It uses landmark analysis to compose annotated tasks and
leverages curriculum learning to order the learning of methods from simpler to
more complex. This eliminates the need for manual input, resolving a core issue
with HTN-MAKER. We prove CURRICULAMA's soundness, and show experimentally that
it has a substantially similar convergence rate in learning a complete set of
methods to HTN-MAKER.
