PlanGenLLMs: A Modern Survey of LLM Planning Capabilities

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 · 1901

Earlier work this paper cites.

Step-by-step: Separating planning from realization in neural data-to-text generation

Original

Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019 · 1904

Earlier work this paper cites.

Elements of a theory of human problem solving

Allen Newell, John Calman Shaw, and Herbert A Simon. 1958 · 1958

Earlier work this paper cites.

Multiple-agent planning systems

Kurt Konolige and Nils J Nilsson. 1980 · 1980

Earlier work this paper cites.

Towards a foundation for evaluating ai planners

Nabil A Kartam and David E Wilkins. 1990 · 1990

Earlier work this paper cites.

Pddl-the planning domain definition language

Drew McDermott, Malik Ghallab, Adele E. Howe, Craig A. Knoblock, Ashwin Ram, Manuela M. Veloso, Daniel S. Weld, and David E. Wilkins. 1998 · 1998

Earlier work this paper cites.

Lpg: A planner based on local search for planning graphs with action costs

Alfonso Gerevini, Ivan Serina, et al. 2002 · 2002

Earlier work this paper cites.

Convex Optimization

Stephen Boyd and Lieven Vandenberghe. 2004 · 2004

Earlier work this paper cites.

Val: Automatic plan validation, continuous effects and mixed initiative planning using pddl

Richard Howey, Derek Long, and Maria Fox. 2004 · 2004

Earlier work this paper cites.

The fast downward planning system

Malte Helmert. 2006 · 2006

Earlier work this paper cites.

Visually-grounded planning without vision: Language models infer detailed plans from high-level instructions

Original

Peter A Jansen. 2020 · 2009

Earlier work this paper cites.

Alfworld: Aligning text and embodied environments for interactive learning

Original

Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. 2020b · 2010

Earlier work this paper cites.

A new representation and associated algorithms for generalized planning

Siddharth Srivastava, Neil Immerman, and Shlomo Zilberstein. 2011 · 2011

Earlier work this paper cites.

Width and inference based planners: Siw, bfs (f), and probe

Nir Lipovetzky, Miquel Ramirez, Christian Muise, and Hector Geffner. 2014 · 2014

Earlier work this paper cites.

Artificial intelligence: a modern approach

Stuart J Russell and Peter Norvig. 2016 · 2016

Earlier work this paper cites.

Cooperative multi-agent planning: A survey

Alejandro Torreno, Eva Onaindia, Antonín Komenda, and Michal Štolba. 2017 · 2017

Earlier work this paper cites.

Virtualhome: Simulating household activities via programs

Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. 2018 · 2018

Earlier work this paper cites.

Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task

Original

Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018

Earlier work this paper cites.

Language models are unsupervised multitask learners

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019

Earlier work this paper cites.

Exploring the limits of transfer learning with a unified text-to-text transformer

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020

Earlier work this paper cites.

The scip optimization suite 8.0

Original

Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, et al. 2021 · 2021

Earlier work this paper cites.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021

Earlier work this paper cites.

Skill induction and planning with latent language

Original

Pratyusha Sharma, Antonio Torralba, and Jacob Andreas. 2021 · 2021

Earlier work this paper cites.

Do as i can, not as i say: Grounding language in robotic affordances

Original

Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2022 · 2022

Earlier work this paper cites.

Pre-trained language models for interactive decision-making

Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al. 2022 · 2022

Earlier work this paper cites.

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 · 2022

Earlier work this paper cites.

Planning with large language models via corrective re-prompting

Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex. 2022 · 2022

Earlier work this paper cites.

Language models are greedy reasoners: A systematic formal analysis of chain-of-thought

Original

Abulhair Saparov and He He. 2022 · 2022

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Earlier work this paper cites.

Pg3: Policy-guided planning for generalized policy generation

Original

Ryan Yang, Tom Silver, Aidan Curtis, Tomas Lozano-Perez, and Leslie Pack Kaelbling. 2022 · 2022

Earlier work this paper cites.

Gpt-4 technical report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023

Earlier work this paper cites.

Can large language models be good path planners? a benchmark and investigation on spatial-temporal reasoning

Original

Mohamed Aghzal, Erion Plaku, and Ziyu Yao. 2023 · 2023

Earlier work this paper cites.

Codeplan: Repository-level coding using llms and planning

Original

Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Vageesh D C, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B. Ashok, and Shashank Shet. 2023 · 2023

Earlier work this paper cites.

Learning to reason over scene graphs: a case study of finetuning gpt-2 into a robot language model for grounded task planning

Georgia Chalvatzaki, Ali Younes, Daljeet Nandha, An Thai Le, Leonardo FR Ribeiro, and Iryna Gurevych. 2023 · 2023

Earlier work this paper cites.

Dynamic planning with a llm

Original

Gautier Dagan, Frank Keller, and Alex Lascarides. 2023 · 2023

Earlier work this paper cites.

Mind2web: Towards a generalist agent for the web

Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. 2023 · 2023

Earlier work this paper 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. 2023 · 2023

Cited alongside, same era.

Reasoning with language model is planning with world model

Original

Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. 2023 · 2023

Cited alongside, same era.

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Original

Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023 · 2023

Cited alongside, same era.

Swe-bench: Can language models resolve real-world github issues?

Original

Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023

Cited alongside, same era.

To the globe (ttg): Towards language-driven guaranteed travel planning

Original

Da Ju, Song Jiang, Andrew Cohen, Aaron Foss, Sasha Mitts, Arman Zharmagambetov, Brandon Amos, Xian Li, Justine T Kao, Maryam Fazel-Zarandi, et al. 2024 · 2024

Later among the works it cites.

Can large language models reason and plan?

Subbarao Kambhampati. 2024 · 2024

Later among the works it cites.

Llms can’t plan, but can help planning in llm-modulo frameworks

Original

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Kaya Stechly, Mudit Verma, Siddhant Bhambri, Lucas Saldyt, and Anil Murthy. 2024 · 2024

Later among the works it cites.

Thought of search: Planning with language models through the lens of efficiency

Michael Katz, Harsha Kokel, Kavitha Srinivas, and Shirin Sohrabi. 2024 · 2024

Later among the works it cites.

Tree search for language model agents

Original

Jing Yu Koh, Stephen McAleer, Daniel Fried, and Ruslan Salakhutdinov. 2024 · 2024