Fetching the paper…
Reading the bibliography…
Unlike perfect information games, where all elements are known to every player, imperfect information games emulate the real-world complexities of decision-making under uncertain or incomplete information.
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 · 1901
Earlier work this paper cites.
Stable-predictive optimistic counterfactual regret minimization
Gabriele Farina, Christian Kroer, Noam Brown, and Tuomas Sandholm · 1902
Earlier work this paper cites.
Games with incomplete information played by “bayesian” players part ii. bayesian equilibrium points
John C Harsanyi · 1968
Earlier work this paper cites.
Does the chimpanzee have a theory of mind?
David Premack and Guy Woodruff · 1978
Earlier work this paper cites.
Reputation and imperfect information
David M Kreps and Robert Wilson · 1982
Earlier work this paper cites.
Efficient and durable decision rules with incomplete information
Bengt Holmström and Roger B Myerson · 1983
Earlier work this paper cites.
The mental simulation debate
Martin Davies · 1994
Earlier work this paper cites.
A theoretical and empirical investigation of search in imperfect information games
Ian Frank and David Basin · 2001
Earlier work this paper cites.
Mindreading: An integrated account of pretence, self-awareness, and understanding other minds
Shaun Nichols and Stephen P Stich · 2003
Earlier work this paper cites.
Theory of mind
Chris Frith and Uta Frith · 2005
Earlier work this paper cites.
Regret minimization in games with incomplete information
Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione · 2007
Earlier work this paper cites.
The shared circuits model (scm): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading
Susan Hurley · 2008
Earlier work this paper cites.
Theory theory (simulation theory, theory of mind)
Louise Röska-Hardy · 2008
Earlier work this paper cites.
Using iterated reasoning to predict opponent strategies
Michael Wunder, Michael Kaisers, John Robert Yaros, and Michael Littman · 2011
Earlier work this paper cites.
Bayes’ bluff: Opponent modelling in poker
Finnegan Southey, Michael P Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, and Chris Rayner · 2012
Earlier work this paper cites.
How much does it help to know what she knows you know? an agent-based simulation study
Harmen De Weerd, Rineke Verbrugge, and Bart Verheij · 2013
Earlier work this paper cites.
Solving large imperfect information games using cfr+
Oskari Tammelin · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Deep reinforcement learning from self-play in imperfect-information games
Johannes Heinrich and David Silver · 2016
Earlier work this paper cites.
Twenty lectures on algorithmic game theory
Tim Roughgarden · 2016
Earlier work this paper cites.
Multi-view 3d models from single images with a convolutional network
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2016
Earlier work this paper cites.
Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisỳ, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
Earlier work this paper cites.
Superhuman ai for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2018
Earlier work this paper cites.
“fatal attraction” and level-k thinking in games with non-neutral frames
Vincent P. Crawford · 2018
Earlier work this paper cites.
Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
Cited alongside, same era.
Deep counterfactual regret minimization
Noam Brown, Adam Lerer, Sam Gross, and Tuomas Sandholm · 2019
Cited alongside, same era.
Rlcard: A toolkit for reinforcement learning in card games
Daochen Zha, Kwei-Herng Lai, Yuanpu Cao, Songyi Huang, Ruzhe Wei, Junyu Guo, and Xia Hu · 2019
Cited alongside, same era.
Human-level performance in no-press diplomacy via equilibrium search
Jonathan Gray, Adam Lerer, Anton Bakhtin, and Noam Brown · 2020
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Cited alongside, same era.
Are chatgpt and gpt-4 good poker players? – a pre-flop analysis, 2023
Akshat Gupta · 2023
Closest in time.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Closest in time.
Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
Closest in time.
Theory of mind may have spontaneously emerged in large language models
Michal Kosinski · 2023
Closest in time.
Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, and Xiang Ren · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Cited alongside, same era.
Douzero: Mastering doudizhu with self-play deep reinforcement learning
Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, and Ji Liu · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Cited alongside, same era.
From images to textual prompts: Zero-shot vqa with frozen large language models
Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, and Steven CH Hoi · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Cited alongside, same era.
Closest in time.
The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al · 2023
Closest in time.
Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman · 2023
Closest in time.
Model card and evaluations for claude models
C. Models · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
Gorilla: Large language model connected with massive apis, 2023
Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Closest in time.
Memory augmented large language models are computationally universal
Dale Schuurmans · 2023
Closest in time.
Algorithm of thoughts: Enhancing exploration of ideas in large language models
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Lu Wang, Ruoxi Jia, and Ming Jin · 2023
Closest in time.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath · 2023
Closest in time.
Abstracting imperfect information away from two-player zero-sum games, 2023
Samuel Sokota, Ryan D’Orazio, Chun Kai Ling, David J. Wu, J. Zico Kolter, and Noam Brown · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Closest in time.
Spring: Gpt-4 out-performs rl algorithms by studying papers and reasoning
Yue Wu, So Yeon Min, Shrimai Prabhumoye, Yonatan Bisk, Ruslan Salakhutdinov, Amos Azaria, Tom Mitchell, and Yuanzhi Li · 2023
Closest in time.
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
Closest in time.
Siren’s song in the ai ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Closest in time.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
Closest in time.
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
Closest in time.
Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date
Meta Llama Team · 2024
Closest in time.
Imperfect-information game ai agent based on reinforcement learning using tree search and a deep neural network
Xin Ouyang and Ting Zhou · 2079
Closest in time.