An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations

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.