This paper describes how to learn Hierarchical Task Networks (HTNs) in nondeterministic planning domains, where actions may have multiple possible outcomes. We discuss several desired properties that guarantee that the resulting HTNs will correctly handle the nondeterminism in the domain. We developed a new learning algorithm, called HTN-MAKER ND , that exploits these properties. We implemented HTN-MAKER ND in the recently-proposed HTN-MAKER system, a goalregression based HTN learning approach.

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... In our theoretical study, we show that HTN-MAKER ND soundly produces HTN planning knowledge in low-order polynomial times, despite the nondeterminism. In our experiments with two nondeterministic planning domains, ND-SHOP2, a well-known HTN planning algorithm for nondeterministic domains, significantly outperformed (in some cases, by about 3 orders of magnitude) the well-known planner MBP using the learned HTNs.