Authors:
Dominik Schreiber
1
;
Damien Pellier
2
;
Humbert Fiorino
2
and
Tomáš Balyo
3
Affiliations:
1
Karlsruhe Institut für Technologie, Karlsruhe, Germany, Université Grenoble-Alpes, Grenoble and France
;
2
Université Grenoble-Alpes, Grenoble and France
;
3
Karlsruhe Institut für Technologie, Karlsruhe and Germany
Keyword(s):
HTN Planning, SAT Planning, Incremental SAT Solving.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
Symbolic Systems
;
Task Planning and Execution
Abstract:
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.