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Planning in a text-based environment continues to be a major challenge for AI systems.
Strips: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson. 1971 · 1971
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Mind in society: Development of higher psychological processes
Lev Semenovich Vygotsky and Michael Cole. 1978 · 1978
Earlier work this paper cites.
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
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Planning algorithms
Steven M LaValle. 2006 · 2006
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Framer: Planning models from natural language action descriptions
Alan Lindsay, Jonathon Read, Joao Ferreira, Thomas Hayton, Julie Porteous, and Peter Gregory. 2017 · 2017
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020 · 2020
Earlier work this paper cites.
Katherine M. Collins, Catherine Wong, Jiahai Feng, Megan Wei, and Joshua B. Tenenbaum. 2022 · 2022
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. 2022 · 2022
Cited alongside, same era.
Pddl planning with pretrained large language models
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2022 · 2022
Cited alongside, same era.
Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022 · 2022
Cited alongside, same era.
Knowledge transfer from high-resource to low-resource programming languages for code llms
Llm+p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023 · 2023
Later among the works it cites.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Later among the works it cites.
Autoplanbench:: Automatically generating benchmarks for llm planners from pddl
Katharina Stein and Alexander Koller. 2023 · 2023
Later among the works it cites.
Learning adaptive planning representations with natural language guidance
Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S Siegel, Jiahai Feng, Noa Korneev, Joshua B Tenenbaum, and Jacob Andreas. 2023 · 2023
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Federico Cassano, John Gouwar, Francesca Lucchetti, Claire Schlesinger, Carolyn Jane Anderson, Michael Greenberg, Abhinav Jangda, and Arjun Guha. 2023 · 2023
Cited alongside, same era.
Grounded decoding: Guiding text generation with grounded models for robot control
Wenlong Huang, Fei Xia, Dhruv Shah, Danny Driess, Andy Zeng, Yao Lu, Pete Florence, Igor Mordatch, Sergey Levine, Karol Hausman, et al. 2023 · 2023
Cited alongside, same era.
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
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)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2023c
Cited in the paper.
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. 2023 · 2023
Later among the works it cites.
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. 2024 · 2024
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