Being able to predict the mental states of others is a key factor to effective social interaction.
Reading the bibliography…
Does the chimpanzee have a theory of mind?
David Premack and Guy Woodruff · 1978
Earlier work this paper cites.
The complexity of decentralized control of markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Children’s strategic theory of mind
Itai Sher, Melissa Koenig, and Aldo Rustichini · 2014
Earlier work this paper cites.
A natural history of human thinking
Michael Tomasello · 2014
Earlier work this paper cites.
Predicting the other in cooperative interactions
Alan G Sanfey, Claudia Civai, and Peter Vavra · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray kavukcuoglu · 2016
Earlier work this paper cites.
Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, arthur szlam, and Rob Fergus · 2016
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Counterfactual multi-agent policy gradients
Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
Cited alongside, same era.
Learning policy representations in multiagent systems
Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, and Harrison Edwards · 2018
Cited alongside, same era.
Machine theory of mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick · 2018
Cited alongside, same era.
Modeling others using oneself in multi-agent reinforcement learning
Roberta Raileanu, Emily Denton, Arthur Szlam, and Rob Fergus · 2018
Cited alongside, same era.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
Cited alongside, same era.
Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2018
Cited alongside, same era.
Satisficing models of bayesian theory of mind for explaining behavior of differently uncertain agents
Socially Interactive Agents Track, Jan Pöppel, and Stefan Kopp · 2018
Cited alongside, same era.
Cooperative object transport in multi-robot systems: A review of the state-of-the-art
Elio Tuci, Muhanad HM Alkilabi, and Otar Akanyeti · 2018
Cited alongside, same era.
On the utility of learning about humans for human-ai coordination
Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, and Anca Dragan · 2019
Cited alongside, same era.
Tarmac: Targeted multi-agent communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
Cited alongside, same era.
Theory of mind and peer cooperation in two play contexts
Evren Etel and Virginia Slaughter · 2019
Cited alongside, same era.
Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
Cited alongside, same era.
Individualized controlled continuous communication model for multiagent cooperative and competitive tasks
Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar · 2019
Later among the works it cites.
Hierarchical cooperative multi-agent reinforcement learning with skill discovery
Jiachen Yang, Igor Borovikov, and Hongyuan Zha · 2019
Later among the works it cites.
The hanabi challenge: A new frontier for ai research
Nolan Bard, Jakob N Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, et al · 2020
Later among the works it cites.
Learning individually inferred communication for multi-agent cooperation
gang Ding, Tiejun Huang, and Zongqing Lu · 2020
Later among the works it cites.
Learning hierarchical teaching in cooperative multiagent reinforcement learning
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, and Jonathan P How · 2020
Later among the works it cites.
Improving multi-agent cooperation using theory of mind
Terence X Lim, Sidney Tio, and Desmond C Ong · 2020
Later among the works it cites.
Learning multi-agent coordination for enhancing target coverage in directional sensor networks
Jing Xu, Fangwei Zhong, and Yizhou Wang · 2020
Later among the works it cites.
Towards distraction-robust active visual tracking
Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, and Yizhou Wang · 2020
Later among the works it cites.
Baby intuitions benchmark (bib): Discerning the goals, preferences, and actions of others
Kanishk Gandhi, Gala Stojnic, Brenden M Lake, and Moira R Dillon · 2021
Closest in time.
Phase: Physically-grounded abstract social events for machine social perception
Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, and Joshua B Tenenbaum · 2021
Closest in time.
Watch-and-help: A challenge for social perception and human-ai collaboration
Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B Tenenbaum, Sanja Fidler, and Antonio Torralba · 2021
Closest in time.
Agent: A benchmark for core psychological reasoning
Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua B Tenenbaum, and Tomer D Ullman · 2021
Closest in time.
Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Sarah A Wu, Rose E Wang, James A Evans, Joshua B Tenenbaum, David C Parkes, and Max Kleiman-Weiner · 2021
Closest in time.
The surprising effectiveness of mappo in cooperative, multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…