Learning Recursive HTN-Method Structures for planning

HTN planning is one of the most effective planning methods in AI. However, designing the HTN-decomposition methods is a very difficult task which has been achieved mainly by humans. It would therefore be desirable to design automated learning methods to acquire these decomposition methods from observed action sequences. In this work, we explore how to apply model-based clustering in order to construct task decomposition hierarchies and summarize a database of action sequences. We present a probabilistic model for unsupervised learning of HTN methods from action sequences. Based on this model, we introduce a novel two-pronged approach by simultaneously learning a Markov model for action segment clusters from action sequences and then learning an action parameter model for recognizing tasks. These models are integrated together to construct action clusters. Then, an abstraction algorithm is applied to extract variables from the action parameters in each cluster to obtain succinct HTN methods. We introduce evaluation metrics for this approach, and test the algorithm in a logistics planning domain.