--- title: "Who cites The Fast Downward Planning — 35 citing papers" description: "35 papers of the Elamur Research library cite «The Fast Downward Planning System», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/helmert-2006-fast-downward-planning-system/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/helmert-2006-fast-downward-planning-system/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The Fast Downward Planning System](https://md.research.elamur.ai/free/en/papers/helmert-2006-fast-downward-planning-system/) / cited by # Who cites «The Fast Downward Planning System» 35 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «The Fast Downward Planning System»](https://md.research.elamur.ai/free/en/papers/helmert-2006-fast-downward-planning-system/) 1. [**ProgPrompt: Generating Situated Robot Task Plans using Large Language Models arXiv:2209.11302**Ishika Singh, Valts Blukis, Arsalan Mousavian, et al.*2022 · 735 citations*](https://md.research.elamur.ai/free/en/papers/singh-2022-progprompt-generating-situated-robot-task/) 2. [**ALFWorld: Aligning Text and Embodied Environments for Interactive Learning arXiv:2010.03768**Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, et al.*2020 · 322 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/) 3. **[PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 222 citations* 4. [**LLM+P: Empowering Large Language Models with Optimal Planning Proficiency arXiv:2304.11477**Bo Liu, Yuqian Jiang, Xiaohan Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-llm-p-empowering-large-language/) 5. **[Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://md.research.elamur.ai/free/en/papers/zhao-2023-large-language-models-commonsense-knowledge/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zirui Zhao, Wee Sun Lee, David Hsu*2023 · 153 citations* 6. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations* 7. **[Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://md.research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lin Guan, Karthik Valmeekam, Sarath Sreedharan, et al.*2023 · 115 citations* 8. **[Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://md.research.elamur.ai/free/en/papers/silver-2023-generalized-planning-pddl-domains-pretrained/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Tom Silver, Soham Dan, Kavitha Srinivas, et al.*2023 · 112 citations* 9. **[PlanGenLLMs: A Modern Survey of LLM Planning Capabilities](https://md.research.elamur.ai/free/en/papers/wei-2025-plangenllms-modern-survey-llm-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Wei, Zihao Zhang, Shenghua He, et al.*2025 · 55 citations* 10. **[SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge](https://md.research.elamur.ai/free/en/papers/hazra-2023-saycanpay-heuristic-planning-large-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt*2023 · 51 citations* 11. **[Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools](https://md.research.elamur.ai/free/en/papers/hao-2025-large-language-models-can-solve/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yilun Hao, Yongchao Chen, Yang Zhang, et al.*2025 · 18 citations* 12. **[Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://md.research.elamur.ai/free/en/papers/pisano-2026-process-reward-models-meet-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Raffaele Pisano, Roberto Navigli*2026 · 1 citation* 23 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/helmert-2006-fast-downward-planning-system/) — The Fast Downward Planning System