Lose the Order, Keep the Hierarchy: Deordering HTN Plans
A paper titled 'Lose the Order, Keep the Hierarchy: Deordering HTN Plans' was published on arXiv on 2026-09-03. It adapts two classical planning deordering techniques to Hierarchical Task Network (HTN) planning, evaluates them on IPC 2023 Partial-Order HTN benchmarks, and compares against the Optiplan planner. Results show substantial reduction in ordering constraints and less pronounced reduction in critical path length.
The paper addresses plan deordering in HTN planning, an under-researched area compared to classical planning. It extends two established deordering techniques to account for hierarchical decomposition constraints. Evaluation on IPC 2023 benchmarks shows substantial reduction in ordering constraints, with smaller improvements in critical path length.
The work demonstrates that classical plan deordering techniques can be adapted to HTN planning by incorporating hierarchical decomposition constraints. The substantial reduction in ordering constraints suggests that HTN plans often contain unnecessary orderings that can be safely removed without invalidating the plan. The less pronounced reduction in critical path length indicates that deordering primarily increases plan flexibility rather than shortening execution time.
HTN planning is used in domains requiring hierarchical task decomposition, such as robotics, manufacturing, and logistics. Improved deordering can lead to more flexible and robust plans, potentially reducing execution time and improving adaptability in dynamic environments. The availability of open benchmarks (IPC 2023) and comparison with Optiplan provides a foundation for further research and practical adoption.
For industries relying on HTN planning, such as automated manufacturing, logistics, and autonomous systems, deordering can lead to more efficient and adaptable plans. This may reduce operational costs and improve responsiveness to changes. The research contributes to the development of more capable planning tools, potentially enhancing productivity in complex task environments.
Future work may explore more sophisticated deordering techniques, integration with HTN plan generation, and application to real-world planning problems. The observed reduction in critical path length, though modest, suggests potential for optimization in time-critical domains. Further evaluation on diverse benchmarks and comparison with other partial-order HTN planners could strengthen the findings.