Built on

PDDL - the planning domain definition language
Malik Ghallab, Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, and David Wilkins. 1998 · 1998
Earlier work this paper cites.
Counting to explore and generalize in text-based games
Original
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, and Adam Trischler. 2019 · 1998
Earlier work this paper cites.
Exploration based language learning for text-based games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga, Piero Molino, Adrien Ecoffet, Huaixiu Zheng, Alexandros Papangelis, Dian Yu, Chandra Khatri, and Gokhan Tur. 2020 · 2020
Earlier work this paper cites.
Goal-oriented script construction
Qing Lyu, Li Zhang, and Chris Callison-Burch. 2021 · 2021
Earlier work this paper cites.
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
Original
Katherine M. Collins, Catherine Wong, Jiahai Feng, Megan Wei, and Joshua B. Tenenbaum. 2022 · 2022
Earlier work this paper cites.

Similar

Textworldexpress: Simulating text games at one million steps per second
Original
Peter A. Jansen and Marc-Alexandre Côté. 2022 · 2022
Cited alongside, same era.
Llm+ p: Empowering large language models with optimal planning proficiency
Original
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023 · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning
Original
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Cited alongside, same era.
Clin: A continually learning language agent for rapid task adaptation and generalization
Original
Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark. 2023 · 2023
Cited alongside, same era.

Then

Generalized planning in pddl domains with pretrained large language models
Original
Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. 2023 · 2023
Later among the works it cites.
Autoplanbench:: Automatically generating benchmarks for llm planners from pddl
Original
Katharina Stein and Alexander Koller. 2023 · 2023
Later among the works it cites.
Learning adaptive planning representations with natural language guidance
Original
Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S Siegel, Jiahai Feng, Noa Korneev, Joshua B Tenenbaum, and Jacob Andreas. 2023 · 2023
Later among the works it cites.
Translating natural language to planning goals with large-language models
Original
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. 2023 · 2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2024

PDDLEGO: Iterative Planning in Textual Environments

Zhang, Li, Jansen, Peter, Zhang, Tianyi et al.

Understand

Planning in textual environments have been shown to be a long-standing challenge even for current models.

A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner.
However, existing methods rely on a fully-observed environment where all entity states are initially known, so a one-off representation can be constructed, leading to a complete plan.
In contrast, we tackle partially-observed environments where there is initially no sufficient information to plan for the end-goal.

Built on

PDDL - the planning domain definition language
Malik Ghallab, Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, and David Wilkins. 1998 · 1998
Earlier work this paper cites.
Counting to explore and generalize in text-based games
Original
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, and Adam Trischler. 2019 · 1998
Earlier work this paper cites.
Exploration based language learning for text-based games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga, Piero Molino, Adrien Ecoffet, Huaixiu Zheng, Alexandros Papangelis, Dian Yu, Chandra Khatri, and Gokhan Tur. 2020 · 2020
Earlier work this paper cites.
Goal-oriented script construction
Qing Lyu, Li Zhang, and Chris Callison-Burch. 2021 · 2021
Earlier work this paper cites.
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
Original
Katherine M. Collins, Catherine Wong, Jiahai Feng, Megan Wei, and Joshua B. Tenenbaum. 2022 · 2022
Earlier work this paper cites.

Similar

Textworldexpress: Simulating text games at one million steps per second
Original
Peter A. Jansen and Marc-Alexandre Côté. 2022 · 2022
Cited alongside, same era.
Llm+ p: Empowering large language models with optimal planning proficiency
Original
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023 · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning
Original
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Cited alongside, same era.
Clin: A continually learning language agent for rapid task adaptation and generalization
Original
Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark. 2023 · 2023
Cited alongside, same era.

Then

Generalized planning in pddl domains with pretrained large language models
Original
Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. 2023 · 2023
Later among the works it cites.
Autoplanbench:: Automatically generating benchmarks for llm planners from pddl
Original
Katharina Stein and Alexander Koller. 2023 · 2023
Later among the works it cites.
Learning adaptive planning representations with natural language guidance
Original
Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S Siegel, Jiahai Feng, Noa Korneev, Joshua B Tenenbaum, and Jacob Andreas. 2023 · 2023
Later among the works it cites.
Translating natural language to planning goals with large-language models
Original
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. 2023 · 2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reflexion: Language agents with verbal reinforcement learning

Original

Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023

Cited alongside, same era.

Language models as zero-shot planners: Extracting actionable knowledge for embodied agents

Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022a

Cited in the paper.

Inner monologue: Embodied reasoning through planning with language models

Original

Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. 2022b

Cited in the paper.

Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change

Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2023a

Cited in the paper.

On the planning abilities of large language models–a critical investigation

Original

Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. 2023b

Cited in the paper.

Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)

Original

Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2023c

Cited in the paper.