  Hierarchical Task Networks (HTN) planners generate plans using a
decomposition process with extra domain knowledge to guide search towards a
planning task. While domain experts develop HTN descriptions, they may
repeatedly describe the same preconditions, or methods that are rarely used or
possible to be decomposed. By leveraging a three-stage compiler design we can
easily support more language descriptions and preprocessing optimizations that
when chained can greatly improve runtime efficiency in such domains. In this
paper we evaluate such optimizations with the HyperTensioN HTN planner, used in
the HTN IPC 2020.

Artificial Intelligence;Multiagent Systems;Systems and Control;Systems and Control