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New techniques deorder HTN plans, reducing ordering constraints

By PulseAugur Editorial · [1 sources] ·

Researchers have adapted two plan deordering techniques from classical planning to the Hierarchical Task Network (HTN) planning domain. These adapted techniques aim to reduce unnecessary ordering constraints between actions in a plan while maintaining its validity. Evaluations on the IPC 2023 Partial-Order HTN benchmarks showed a significant reduction in ordering constraints and a less pronounced reduction in critical path length when compared to the Optiplan planner. AI

IMPACT This research could lead to more efficient planning algorithms in AI systems that rely on hierarchical task decomposition.

RANK_REASON The cluster contains an academic paper detailing new methods for Hierarchical Task Network planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New techniques deorder HTN plans, reducing ordering constraints

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The cluster contains an academic paper detailing new methods for Hierarchical Task Network planning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Takudzwa Togarepi, Gaspard Quenard, Damien Pellier, Humbert Fiorino ·

    Lose the Order, Keep the Hierarchy: Deordering HTN Plans

    arXiv:2609.03912v1 Announce Type: new Abstract: Hierarchical Task Network (HTN) planning is a powerful planning formalism based on task decomposition. Although most of the literature studied plan generation, comparatively less attention has been paid to post-plan optimization. In…

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