An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations

M. Grand; H. Fiorino; D. Pellier

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arXiv · 2206.06882

An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations

M. Grand·H. Fiorino·D. Pellier

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Abstract

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.

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BibTeX RIS

M. Grand, H. Fiorino, D. Pellier. 2022-06-14. An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations. https://arxiv.org/abs/2206.06882

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