Efficient SAT Encodings for Hierarchical Planning

Dominik Schreiber (1) , Damien Pellier (1) , Humbert Fiorino (1) , Tomáš Balyo

1 Marvin - Artificial Intelligence and Robotics

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.

Mots clés: HTN Planning; SAT Planning; Incremental SAT Solving

Domaines: Intelligence artificielle [cs.AI]

Dominik Schreiber, Damien Pellier, Humbert Fiorino, Tomáš Balyo. Efficient SAT Encodings for Hierarchical Planning. 11th International Conference on Agents and Artificial Intelligence (ICAART 2019), Feb 2019, Prague, Czech Republic. ⟨hal-02073463⟩