Hierarchical Task Networks (HTN) are one of the most expressive representations for automated planning
problems. On the other hand, in recent years, the performance of SAT solvers has been drastically improved.
To take advantage of these advances, we investigate how to encode HTN problems as SAT problems. In
this paper, we propose two new encodings: GCT (Grammar-Constrained Tasks) and SMS (Stack Machine
Simulation), which, contrary to previous encodings, address recursive task relationships in HTN problems. We
evaluate both encodings on benchmark domains from the International Planning Competition (IPC), setting a
new baseline in SAT planning on modern HTN domains.