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In recent years, large language models (LLMs) have shown remarkable capabilities in various artificial intelligence problems.
Some studies in machine learning using the game of checkers
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Experiments with the graph traverser program
James E. Doran and Donald Michie · 1966
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A formal basis for the heuristic determination of minimum cost paths
Peter E. Hart, Nils J. Nilsson, and Bertram Raphael · 1968
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First results on the effect of error in heuristic search
Ira Pohl · 1969
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STRIPS: A new approach to the application of theorem proving to problem solving
Richard E. Fikes and Nils J. Nilsson · 1971
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Heuristics: Intelligent Search Strategies for Computer Problem Solving
Judea Pearl · 1984
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A unified theory of heuristic evaluation functions and its application to learning
Jens Christensen and Richard E Korf · 1986
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On the complexity of blocks-world planning
Naresh Gupta and Dana S. Nau · 1992
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Sokoban is PSPACE-complete
Joseph C. Culberson · 1997
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The 1998 AI Planning Systems competition
Drew McDermott · 2000
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Planning as heuristic search
Blai Bonet and Héctor Geffner · 2001
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The FF planning system: Fast plan generation through heuristic search
Jörg Hoffmann and Bernhard Nebel · 2001
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VAL’s progress: The automatic validation tool for PDDL2.1 used in the International Planning Competition
Richard Howey and Derek Long · 2003
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Automated Planning: Theory and Practice
Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
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A planning heuristic based on causal graph analysis
Malte Helmert · 2004
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The Fast Downward planning system
Malte Helmert · 2006
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Approximation properties of planning benchmarks
Malte Helmert, Robert Mattmüller, and Gabi Röger · 2006
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Unifying the causal graph and additive heuristics
Malte Helmert and Héctor Geffner · 2008
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Landmarks revisited
Silvia Richter, Malte Helmert, and Matthias Westphal · 2008
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Learning from multiple heuristics
Mehdi Samadi, Ariel Felner, and Jonathan Schaeffer · 2008
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Preferred operators and deferred evaluation in satisficing planning
Silvia Richter and Malte Helmert · 2009
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The LAMA planner: Guiding cost-based anytime planning with landmarks
Silvia Richter and Matthias Westphal · 2010
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The more, the merrier: Combining heuristic estimators for satisficing planning
Gabriele Röger and Malte Helmert · 2010
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Learning heuristic functions for large state spaces
Shahab J. Arfaee, Sandra Zilles, and Robert C. Holte · 2011
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Weisfeiler-Lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Red-black planning: A new systematic approach to partial delete relaxation
Carmel Domshlak, Jörg Hoffmann, and Michael Katz · 2015
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Understanding the search behaviour of greedy best-first search
Manuel Heusner, Thomas Keller, and Malte Helmert · 2017
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Best-first width search: Exploration and exploitation in classical planning
Nir Lipovetzky and Hector Geffner · 2017
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Optimal solutions to large logistics planning domain problems
Gerald Paul, Gabriele Röger, Thomas Keller, and Malte Helmert · 2017
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Downward Lab
Jendrik Seipp, Florian Pommerening, Silvan Sievers, and Malte Helmert · 2017
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Symbolic perimeter abstraction heuristics for cost-optimal planning
Álvaro Torralba, Carlos Linares López, and Daniel Borrajo · 2018
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An Introduction to the Planning Domain Definition Language , volume 13 of Synthesis Lectures on Artificial Intelligence and Machine Learning
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Understanding sample generation strategies for learning heuristic functions in classical planning
Rafael V Bettker, Pedro P Minini, André G Pereira, and Marcus Ritt · 2024
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Exploring and benchmarking the planning capabilities of large language models
Bernd Bohnet, Azade Nova, Aaron T Parisi, Kevin Swersky, Katayoon Goshvadi, Hanjun Dai, Dale Schuurmans, Noah Fiedel, and Hanie Sedghi · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Learning domain-independent heuristics for grounded and lifted planning
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Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, and Christian Muise · 2019
Cited alongside, same era.
Pyperplan
Yusra Alkhazraji, Matthias Frorath, Markus Grützner, Malte Helmert, Thomas Liebetraut, Robert Mattmüller, Manuela Ortlieb, Jendrik Seipp, Tobias Springenberg, Philip Stahl, and Jan Wülfing · 2020
Cited alongside, same era.
Neural network heuristics for classical planning: A study of hyperparameter space
Patrick Ferber, Malte Helmert, and Jörg Hoffmann · 2020
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Saturated cost partitioning for optimal classical planning
Jendrik Seipp, Thomas Keller, and Malte Helmert · 2020
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Learning domain-independent planning heuristics with hypergraph networks
William Shen, Felipe Trevizan, and Sylvie Thiébaux · 2020
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Automatic instance generation for classical planning
Álvaro Torralba, Jendrik Seipp, and Silvan Sievers · 2021
Cited alongside, same era.
Neural network heuristic functions for classical planning: Bootstrapping and comparison to other methods
Patrick Ferber, Florian Geißer, Felipe Trevizan, Malte Helmert, and Jörg Hoffmann · 2022
Cited alongside, same era.
Dillon Z. Chen, Sylvie Thiébaux, and Felipe Trevizan · 2024
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Return to tradition: Learning reliable heuristics with classical machine learning
Dillon Z. Chen, Felipe Trevizan, and Sylvie Thiébaux · 2024
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DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J.L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang, Jin Chen, and Jingchang Chen et al · 2024
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NL2Plan: Robust LLM-driven planning from minimal text descriptions
Elliot Gestrin, Marco Kuhlmann, and Jendrik Seipp · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team Google, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, Soroosh Mariooryad, Yifan Ding, Xinyang Geng, Fred Alcober, Roy Frostig, Mark Omernick, Lexi Walker, Cosmin Paduraru, Christina Sorokin, Andrea Tacchetti, Colin Gaffney, Samira Daruki, Olcan Sercinoglu, Zach Gleicher, and Juliette Love et al · 2024
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Guiding GBFS through learned pairwise rankings
Mingyu Hao, Felipe Trevizan, Sylvie Thiébaux, Patrick Ferber, and Jörg Hoffmann · 2024
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Chasing progress, not perfection: Revisiting strategies for end-to-end LLM plan generation
Sukai Huang, Trevor Cohn, and Nir Lipovetzky · 2024
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Thought of search: Planning with language models through the lens of efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas, and Shirin Sohrabi · 2024
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Large language models as planning domain generators
James T. Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, and Shirin Sohrabi · 2024
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Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, and Alhussein Fawzi · 2024
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Learning general policies for planning through GPT models
Nicholas Rossetti, Massimiliano Tummolo, Alfonso Emilio Gerevini, Luca Putelli, Ivan Serina, Mattia Chiari, and Matteo Olivato · 2024
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Dissecting Scorpion: Ablation study of an optimal classical planner
Jendrik Seipp · 2024
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Algorithm of thoughts: enhancing exploration of ideas in large language models
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin · 2024
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Generalized planning in PDDL domains with pretrained large language models
Tom Silver, Soham Dan, Kavitha Srinivas, Josh Tenenbaum, Leslie Pack Kaelbling, and Michael Katz · 2024
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Chain of thoughtlessness? an analysis of CoT in planning
Kaya Stechly, Karthik Valmeekam, and Subbarao Kambhampati · 2024
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The 2023 International Planning Competition
Ayal Taitler, Ron Alford, Joan Espasa, Gregor Behnke, Daniel Fišer, Michael Gimelfarb, Florian Pommerening, Scott Sanner, Enrico Scala, Dominik Schreiber, Javier Segovia-Aguas, and Jendrik Seipp · 2024
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Karthik Valmeekam, Kaya Stechly, Atharva Gundawar, and Subbarao Kambhampati · 2024
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Code and experiment data from the NeurIPS 2025 paper “Classical planning with LLM-generated heuristics: Challenging the state of the art with Python code”
Augusto B. Corrêa, André G. Pereira, and Jendrik Seipp · 2025
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Deepseek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning
DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z.F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, and Guanting Chen et al · 2025
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LLMs can plan only if we tell them
Bilgehan Sel, Ruoxi Jia, and Ming Jin · 2025
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LLMs as planning formalizers: A survey for leveraging large language models to construct automated planning models
Marcus Tantakoun, Christian Muise, and Xiaodan Zhu · 2025
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LLM-generated heuristics for AI planning: Do we even need domain-independence anymore?
Alexander Tuisov, Yonatan Vernik, and Alexander Shleyfman · 2025
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