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There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks.
Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services
Doyle, J. and Patil, R. S · 1991
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International planning competition, 1998
IPC · 1998
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Pddl-the planning domain definition language
McDermott, D., Ghallab, M., Howe, A. E., Knoblock, C. A., Ram, A., Veloso, M. M., Weld, D. S., and Wilkins, D. E · 1998
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Gipo: an integrated graphical tool to support knowledge engineering in ai planning
Simpson, R., McCluskey, T. L., and Zhao, W · 2001
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Activity planning for the mars exploration rovers
Bresina, J. L., Jónsson, A. K., Morris, P. H., and Rajan, K · 2004
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Automated Planning: theory and practice
Ghallab, M., Nau, D., and Traverso, P · 2004
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VAL: Automatic plan validation, continuous effects and mixed initiative planning using PDDL
Howey, R., Long, D., and Fox, M · 2004
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On sat modulo theories and optimization problems
Nieuwenhuis, R. and Oliveras, A · 2006
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A tutorial on planning graph based reachability heuristics
Bryce, D. and Kambhampati, S · 2007
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Model-lite planning for the web age masses: The challenges of planning with incomplete and evolving domain models
Kambhampati, S · 2007
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Artificial intelligence a modern approach
Russell, S. J. and Norvig, P · 2010
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M · 2010
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Thinking, fast and slow
Kahneman, D · 2011
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Automated scheduling for nasa’s deep space network
Johnston, M. D., Tran, D., Arroyo, B., Sorensen, S., Tay, P., Carruth, B., Coffman, A., and Wallace, M · 2014
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Polanyi’s revenge and AI’s new romance with tacit knowledge
Kambhampati, S · 2021
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Gpt3-to-plan: Extracting plans from text using gpt-3
Olmo, A., Sreedharan, S., and Kambhampati, S · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al · 2022
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Leveraging approximate symbolic models for reinforcement learning via skill diversity
Guan, L., Sreedharan, S., and Kambhampati, S · 2022
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Inner monologue: Embodied reasoning through planning with language models
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al · 2022
Cited alongside, same era.
Reward design with language models
Kwon, M., Xie, S. M., Bullard, K., and Sadigh, D · 2022
Cited alongside, same era.
Introducing chatgpt by openai, 2022
OpenAI · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
PDDL planning with pretrained large language models
Silver, T., Hariprasad, V., Shuttleworth, R. S., Kumar, N., Lozano-Pérez, T., and Kaelbling, L. P · 2022
Cited alongside, same era.
Self-instruct: Aligning language model with self generated instructions
Eureka: Human-level reward design via coding large language models
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2023
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McCoy, R. T., Yao, S., Friedman, D., Hardy, M., and Griffiths, T. L · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning
Pan, L., Albalak, A., Wang, X., and Wang, W. Y · 2023
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Saynav: Grounding large language models for dynamic planning to navigation in new environments
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Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H · 2022
Cited alongside, same era.
Learning and leveraging verifiers to improve planning capabilities of pre-trained language models
Arora, D. and Kambhampati, S · 2023
Cited alongside, same era.
Codeplan: Repository-level coding using llms and planning
Bairi, R., Sonwane, A., Kanade, A., Iyer, A., Parthasarathy, S., Rajamani, S., Ashok, B., Shet, S., et al · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
Cited alongside, same era.
Faith and fate: Limits of transformers on compositionality
Dziri, N., Lu, X., Sclar, M., Li, X. L., Jiang, L., Lin, B. Y., Welleck, S., West, P., Bhagavatula, C., Bras, R. L., Hwang, J. D., Sanyal, S., Ren, X., Ettinger, A., Harchaoui, Z., and Choi, Y · 2023
Cited alongside, same era.
Large language models are not abstract reasoners
Gendron, G., Bao, Q., Witbrock, M., and Dobbie, G · 2023
Cited alongside, same era.
Leveraging pre-trained large language models to construct and utilize world models for model-based task planning
Guan, L., Valmeekam, K., Sreedharan, S., and Kambhampati, S · 2023
Cited alongside, same era.
Rajvanshi, A., Sikka, K., Lin, X., Lee, B., Chiu, H.-P., and Velasquez, A · 2023
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al · 2023
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. R., and Yao, S · 2023
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GPT-4 Doesn’t Know It’s Wrong: An Analysis of Iterative Prompting for Reasoning Problems
Stechly, K., Marquez, M., and Kambhampati, S · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Ullman, T · 2023
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Can large language models really improve by self-critiquing their own plans?
Valmeekam, K., Marquez, M., and Kambhampati, S · 2023
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Large language models are better reasoners with self-verification
Weng, Y., Zhu, M., Xia, F., Li, B., He, S., Liu, S., Sun, B., Liu, K., and Zhao, J · 2023
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Translating natural language to planning goals with large-language models
Xie, Y., Yu, C., Zhu, T., Bai, J., Gong, Z., and Soh, H · 2023
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Efficient reinforcement learning via large language model-based search, 2024
Bhambri, S., Bhattacharjee, A., Liu, H., and Kambhampati, S · 2024
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”task success” is not enough: Investigating the use of video-language models as behavior critics for catching undesirable agent behaviors, 2024
Guan, L., Zhou, Y., Liu, D., Zha, Y., Amor, H. B., and Kambhampati, S · 2024
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Robust planning with llm-modulo framework: Case study in travel planning
Gundawar, A., Verma, M., Guan, L., Valmeekam, K., Bhambri, S., and Kambhampati, S · 2024
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Can LLMs reason and plan?
Kambhampati, S · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Travelplanner: A benchmark for real-world planning with language agents
Xie, J., Zhang, K., Chen, J., Zhu, T., Lou, R., Tian, Y., Xiao, Y., and Su, Y · 2024
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