  The Hierarchical Task Network ({\sf HTN}) formalism is very expressive and
used to express a wide variety of planning problems. In contrast to the
classical {\sf STRIPS} formalism in which only the action model needs to be
specified, the {\sf HTN} formalism requires to specify, in addition, the tasks
of the problem and their decomposition into subtasks, called {\sf HTN} methods.
For this reason, hand-encoding {\sf HTN} problems is considered more difficult
and more error-prone by experts than classical planning problem. To tackle this
problem, we propose a new approach (HierAMLSI) based on grammar induction to
acquire {\sf HTN} planning domain knowledge, by learning action models and {\sf
HTN} methods with their preconditions. Unlike other approaches, HierAMLSI is
able to learn both actions and methods with noisy and partial inputs
observation with a high level or accuracy.
