Landmark Generation in HTN Planning Revisited

In Hierarchical Task Network (HTN) planning, landmarks are facts that must hold true, and tasks or methods that must be included in every solution. Existing landmark generation techniques for HTN planning rely on the Delete and Ordering Free (DOF) relaxation and are known to be sound but incomplete, primarily due to the limitations introduced by Task Insertion. This paper presents a new landmark generation method that builds on a previous AND/OR graph-based approach, extending it to capture additional hierarchical dependencies among tasks and methods. We prove that our approach is sound and dominates existing techniques, though it remains incomplete under the DOF relaxation. Experimental results on IPC benchmarks for totally ordered problems show that our method identifies significantly more task and method landmarks across most domains, improving coverage with minimal computational overhead.

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Publication Details

Journal
Proceedings of the International Conference on Automated Planning and Scheduling
Published
2025-09-16
DOI
https://doi.org/10.1609/icaps.v35i1.36123
Citations
1
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
0.73

Funders

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article

Landmark Generation in HTN Planning Revisited

André G. Pereira, Felipe Meneguzzi, Victor Scherer Putrich
1 citations
Proceedings of the International Conference on Automated Planning and Scheduling
Energy Harvesting in Wireless Networks
0.73
article

Landmark Generation in HTN Planning Revisited

André G. Pereira, Felipe Meneguzzi, Victor Scherer Putrich
article en
1 citations

Abstract

In Hierarchical Task Network (HTN) planning, landmarks are facts that must hold true, and tasks or methods that must be included in every solution. Existing landmark generation techniques for HTN planning rely on the Delete and Ordering Free (DOF) relaxation and are known to be sound but incomplete, primarily due to the limitations introduced by Task Insertion. This paper presents a new landmark generation method that builds on a previous AND/OR graph-based approach, extending it to capture additional hierarchical dependencies among tasks and methods. We prove that our approach is sound and dominates existing techniques, though it remains incomplete under the DOF relaxation. Experimental results on IPC benchmarks for totally ordered problems show that our method identifies significantly more task and method landmarks across most domains, improving coverage with minimal computational overhead.

Proceedings of the International Conference on Automated Planning and SchedulingVol. 35(1)
Universidade Federal do Rio Grande do Sul (BR), Pontifícia Universidade Católica do Rio Grande do Sul (BR), University of Rio Grande and Rio Grande Community College (US)
Deutsche Forschungsgemeinschaft, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
Openalex Percentile: Top 23%
Energy Harvesting in Wireless Networks
0.73
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