  Many planning techniques have been developed to allow autonomous systems to
act and make decisions based on their perceptions of the environment. Among
these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the
most used in practice. Unlike classical approaches of planning. HTN operates by
decomposing task into sub-tasks until each of these sub-tasks can be achieved
an action. This hierarchical representation provide a richer representation of
planning problems and allows to better guide the plan search and provides more
knowledge to the underlying algorithms. In this paper, we propose a new
approach of HTN planning in which, as in conventional planning, we instantiate
all planning operators before starting the search process. This approach has
proven its effectiveness in classical planning and is necessary for the
development of effective heuristics and encoding planning problems in other
formalism such as CSP or SAT. The instantiation is actually used by most modern
planners but has never been applied in an HTN based planning framework. We
present in this article a generic instantiation algorithm which implements many
simplification techniques to reduce the process complexity inspired from those
used in classical planning. Finally we present some results obtained from an
experimentation on a range of problems used in the international planning
competitions with a modified version of SHOP planner using fully instantiated
problems.

Artificial Intelligence