Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) are capable of transforming natural language domain descriptions into plausibly looking PDDL markup.
“Pddl— the planning domain definition language”
Constructions Aeronautiques et al · 1998
Earlier work this paper cites.
“The fast downward planning system”
Malte Helmert · 2006
Earlier work this paper cites.
“Goal reasoning: Foundations, emerging applications, and prospects”
David Aha · 2018
Earlier work this paper cites.
“Goal reasoning in the clips executive for integrated planning and execution”
Tim Niemueller, Till Hofmann and Gerhard Lakemeyer · 2019
Earlier work this paper cites.
“Planning Domain Repair as a Diagnosis Problem”
Songtuan Lin, Alban Grastien and Pascal Bercher · 2022
Earlier work this paper cites.
“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
Earlier work this paper cites.
“Language models as zero-shot planners: Extracting actionable knowledge for embodied agents”
Wenlong Huang, Pieter Abbeel, Deepak Pathak and Igor Mordatch · 2022
Earlier work this paper cites.
“PDDL planning with pretrained large language models”
Tom Silver et al · 2022
Earlier work this paper cites.
“Planning with large language models via corrective re-prompting”
Shreyas Raman et al · 2022
Earlier work this paper cites.
“Openagents: An open platform for language agents in the wild”
Tianbao Xie et al · 2023
Earlier work this paper cites.
“A survey on large language model based autonomous agents”
Lei Wang et al · 2023
Cited alongside, same era.
“AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers”
Yongchao Chen et al · 2023
Cited alongside, same era.
“CoPAL: Corrective Planning of Robot Actions with Large Language Models”, 2023
Frank Joublin et al · 2023
Cited alongside, same era.
“Translating natural language to planning goals with large-language models”
Yaqi Xie et al · 2023
Cited alongside, same era.
“Text2motion: From natural language instructions to feasible plans”
“Survey of hallucination in natural language generation”
Ziwei Ji et al · 2023
Later among the works it cites.
“Step-Back Prompting Enables Reasoning Via Abstraction in Large Language Models”
Huaixiu Zheng et al · 2023
Later among the works it cites.
“Llm-planner: Few-shot grounded planning for embodied agents with large language models”
Chan Song et al · 2023
Later among the works it cites.
“Cape: Corrective actions from precondition errors using large language models”
Shreyas Raman et al · 2023
Later among the works it cites.
“Progprompt: Generating situated robot task plans using large language models”
Ishika Singh et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kevin Lin et al · 2023
Cited alongside, same era.
Lionel Wong et al · 2023
Cited alongside, same era.
“Exploring the Limitations of using Large Language Models to Fix Planning Tasks”, 2023
Alba Gragera and Alberto Pozanco · 2023
Cited alongside, same era.
“A planning approach to repair domains with incomplete action effects”
Alba Gragera, Raquel Fuentetaja, Ángel García-Olaya and Fernando Fernández · 2023
Cited alongside, same era.
“Towards automated modeling assistance: An efficient approach for repairing flawed planning domains”
Songtuan Lin, Alban Grastien and Pascal Bercher · 2023
Cited alongside, same era.
Md Sakib and Yu Sun · 2023
Later among the works it cites.
“Leveraging pre-trained large language models to construct and utilize world models for model-based task planning”
Lin Guan, Karthik Valmeekam, Sarath Sreedharan and Subbarao Kambhampati · 2024
Closest in time.
“Grammar prompting for domain-specific language generation with large language models”
Bailin Wang et al · 2024
Closest in time.
“Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability”
Md Sakib and Yu Sun · 2024
Closest in time.