In artificial intelligence, hierarchical task network (HTN) planning is an approach to automated planning in which the dependency among actions can be given in the form of hierarchically structured networks.
Such a network can be used as the precondition for another compound or goal task to be feasible.
One particular formalism for representing hierarchical task networks that has been fairly widely used is TAEMS.
Some of the best-known domain-independent HTN-planning systems are: HTN planning is strictly more expressive than STRIPS, to the point of being undecidable in the general case.
[10] However, many syntactic restrictions of HTN planning are decidable, with known complexities ranging from NP-complete to 2-EXPSPACE-complete,[11] and some HTN problems can be efficiently compiled into PDDL, a STRIPS-like language.