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Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment.
PDDL – the planning domain definition language
Constructions Aeronautiques, Adele Howe, Craig Knoblock, ISI Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, David Wilkins SRI, Anthony Barrett, Dave Christianson, et al · 1998
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Val’s progress: The automatic validation tool for pddl2.1 used in the international planning competition
R. Howey and D. Long · 2003
Earlier work this paper cites.
Automated Planning: Theory and Practice
Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
Earlier work this paper cites.
The fast downward planning system
Malte Helmert · 2006
Earlier work this paper cites.
Domain independent approaches for finding diverse plans
Biplav Srivastava, Tuan Anh Nguyen, Alfonso Gerevini, Subbarao Kambhampati, Minh Binh Do, and Ivan Serina · 2007
Earlier work this paper cites.
Lm-cut: Optimal planning with the landmark-cut heuristic
Malte Helmert and Carmel Domshlak · 2011
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Earlier work this paper cites.
Reshaping diverse planning
Michael Katz and Shirin Sohrabi · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
Earlier work this paper cites.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Johan Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Venkatesh Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
Cited alongside, same era.
Controllable protein design with language models
Noelia Ferruz and Birte Höcker · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey, 2022
Jie Huang and Kevin Chen-Chuan Chang · 2022
Cited alongside, same era.
International planning competitions at international conference on automated planning and scheduling (icaps)
ICAPS · 2022
Cited alongside, same era.
Language models: Past, present, and future
Hang Li · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman · 2022
Later among the works it cites.
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
Later among the works it cites.
Are llms the master of all trades?: Exploring domain-agnostic reasoning skills of llms
Shrivats Agrawal · 2023
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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
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Gpt-4 technical report, 2023
OpenAI · 2023
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Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al · 2022
Cited alongside, same era.
PDDL generators
Jendrik Seipp, Álvaro Torralba, and Jörg Hoffmann · 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
Cited alongside, same era.
Progprompt: Generating situated robot task plans using large language models, 2022
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2022
Cited alongside, same era.
Learning functional properties of proteins with language models
Serbulent Unsal, Heval Atas, Muammer Albayrak, Kemal Turhan, Aybar C Acar, and Tunca Doğan · 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
Cited in the paper.
Closest in time.
Ai-assisted coding: Experiments with gpt-4, 2023
Russell A Poldrack, Thomas Lu, and Gašper Beguš · 2023
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Is chatgpt the ultimate programming assistant–how far is it?
Haoye Tian, Weiqi Lu, Tsz On Li, Xunzhu Tang, Shing-Chi Cheung, Jacques Klein, and Tegawendé F Bissyandé · 2023
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Karthik Valmeekam, Sarath Sreedharan, Matthew Marquez, Alberto Olmo, and Subbarao Kambhampati · 2023
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Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang · 2023
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Translating natural language to planning goals with large-language models
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh · 2023
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A survey of large language models, 2023
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
Closest in time.