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Strategic reasoning is a complex yet essential capability for intelligent agents.
Dealing with non-stationarity in multi-agent deep reinforcement learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, and Stefano V Albrecht. 2019 · 1906
Earlier work this paper cites.
The general theory of employment
John Maynard Keynes. 1936 · 1936
Earlier work this paper cites.
Concours résultats complets
Alain Ledoux. 1981 · 1981
Earlier work this paper cites.
Unraveling in guessing games: An experimental study
Rosemarie Nagel. 1995 · 1995
Earlier work this paper cites.
On players’ models of other players: Theory and experimental evidence
Dale O Stahl and Paul W Wilson. 1995 · 1995
Earlier work this paper cites.
One, two,(three), infinity,…: Newspaper and lab beauty-contest experiments
Antoni Bosch-Domenech, Jose G Montalvo, Rosemarie Nagel, and Albert Satorra. 2002 · 2002
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson. 2016 · 2016
Earlier work this paper cites.
Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III. 2016 · 2016
Earlier work this paper cites.
Value-decomposition networks for cooperative multi-agent learning
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al. 2017 · 2017
Earlier work this paper cites.
Emergent communication through negotiation
Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, and Stephen Clark. 2018 · 2018
Earlier work this paper cites.
Maven: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson. 2019 · 2019
Earlier work this paper cites.
Challenges of reinforcement learning
Zihan Ding and Hao Dong. 2020 · 2020
Earlier work this paper cites.
Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, and Peter Clark. 2021 · 2021
Earlier work this paper cites.
K-level reasoning for zero-shot coordination in hanabi
Brandon Cui, Hengyuan Hu, Luis Pineda, and Jakob Foerster. 2021 · 2021
Earlier work this paper cites.
A diverse corpus for evaluating and developing english math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2021 · 2021
Earlier work this paper cites.
Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2021
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Deep multiagent reinforcement learning: Challenges and directions
Annie Wong, Thomas Bäck, Anna V Kononova, and Aske Plaat. 2021 · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
War and peace (waragent): Large language model-based multi-agent simulation of world wars
Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang. 2023 · 2023
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Yang Li, Yangyang Yu, Haohang Li, Zhi Chen, and Khaldoun Khashanah. 2023 · 2023
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Large language models play starcraft ii: Benchmarks and a chain of summarization approach
Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, and Jun Wang. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. 2022 · 2022
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Commonsenseqa 2.0: Exposing the limits of ai through gamification
Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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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
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. 2023 · 2023
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Toxicity in chatgpt: Analyzing persona-assigned language models
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Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023 · 2023
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Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Tao Ge, and Furu Wei. 2023 · 2023
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Skeleton-of-thought: Large language models can do parallel decoding
Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023 · 2023
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Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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Competeai: Understanding the competition behaviors in large language model-based agents
Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie. 2023 · 2023
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Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations
Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, and Kaidi Xu. 2024 · 2024
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Magic: Investigation of large language model powered multi-agent in cognition, adaptability, rationality and collaboration
Lin Xu, Zhiyuan Hu, Daquan Zhou, Hongyu Ren, Zhen Dong, Kurt Keutzer, See-Kiong Ng, and Jiashi Feng. 2023a · 2024
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SOTOPIA: Interactive evaluation for social intelligence in language agents
Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, and Maarten Sap. 2024 · 2024
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