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
PIE-APT introduces a unified framework for abductive planning over Temporal Dynamic Knowledge Graphs (TDKGs) using two modules: PIE-Abducer, which performs incremental direct-derivation abduction, and PIE-APT, which interleaves backward‑chaining A* search with PIE-Abducer to generate action sequences and abductive assumptions. The approach operates natively on the expressive SROIQ Description Logic, leveraging an incremental reasoner to maintain decidability and bypass the Ramification Problem. Evaluation on four OWL benchmarks demonstrates qualitative superiority over classical planners and shows that the direct‑derivation method outperforms a Minimal Hitting Set baseline in abductive enrichment.
By Amir Hossein Sharafi, Alireza Shahbazi
arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.
By Irene Brugnara, Alessandro Valentini, Andrea Micheli
arXiv:2606. 00618v2 Announce Type: replace Abstract: Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution.
By Robert Gieselmann, Mihai Samson, Federico Pecora, Jeremy L. Wyatt
arXiv:2606. 00618v1 Announce Type: new Abstract: Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution.
By Robert Gieselmann, Mihai Samson, Federico Pecora, Jeremy L. Wyatt
arXiv:2604. 11912v2 Announce Type: replace-cross Abstract: While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reasoning tasks.
By Jianhao Huang, Zhanpeng Zhou, Renqiu Xia, Baharan Mirzasoleiman, Weijie Su, Wei Huang
arXiv:2108.11204v4 Announce Type: replace-cross
Abstract: Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propo...
By Konrad Czechowski, Tomasz Odrzyg\'o\'zd\'z, Marek Zbysi\'nski, Micha{\l} Zawalski, Krzysztof Olejnik, Yuhuai Wu, {\L}ukasz Kuci\'nski, Piotr Mi{\l}o\'s