The paper "Lose the Order, Keep the Hierarchy: Deordering HTN Plans" adapts two classical plan deordering techniques to the Hierarchical Task Network (HTN) planning framework, extending them to respect hierarchical decomposition constraints. The authors evaluate their methods on the IPC 2023 Partial-Order HTN benchmarks and compare them with Optiplan, an HTN planner that generates partially ordered plans directly. Results show a substantial reduction in ordering constraints, with a smaller but noticeable decrease in critical path length.
By Takudzwa Togarepi, Gaspard Quenard, Damien Pellier, Humbert Fiorino
arXiv:2606. 02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning.
By Windy Phung, Dominik Drexler, Arnaud Lequen, Jendrik Seipp
arXiv:2501. 18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning.
By Alexander Tuisov, Yonatan Vernik, Alexander Shleyfman
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
By Jorge Mendez-Mendez
arXiv:2608. 16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans.
By Veit Laule, Jiangtao Shuai, Manfred Hauswirth, Sonja Schimmler
arXiv:2602.00276v3 Announce Type: replace
Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning...
By Aditya Kumar, William W. Cohen