Towards inducing HTN domain models from examples

Domain modelling for AI Planning can be a complex process especially if there is a large number of objects or actions or both to be modelled. This task can be facilitated by tools which induce operators or methods from examples. Further, large and complex domains are more easily constructed if domain languages are used which allow for hierarchical decomposition of domain components. Examples of such a decomposition are object class hierarchies and method hierarchies. This paper describes ongoing work which aims to produce algorithms which learn effective hierarchical decompositions from examples.