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In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents.
Embodied question answering in photorealistic environments with point cloud perception
Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra · 1904
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Planning as search: A quantitative approach
Richard E Korf · 1987
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A formal theory of plan recognition and its implementation
Henry A Kautz · 1991
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Collaborative plans for complex group action
Barbara Grosz and Sarit Kraus · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Altruistic helping in human infants and young chimpanzees
Felix Warneken and Michael Tomasello · 2006
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Socially intelligent robots: dimensions of human–robot interaction
Kerstin Dautenhahn · 2007
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Human-robot interaction: a survey
Michael A Goodrich and Alan C Schultz · 2007
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Plan recognition as planning
Miquel Ramırez and Hector Geffner · 2009
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Help or hinder: Bayesian models of social goal inference
Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, and Joshua B Tenenbaum · 2009
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A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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Activity forecasting
Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert · 2012
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Lecture 6.5—rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinto · 2012
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Understanding collective activitiesof people from videos
Wongun Choi and Silvio Savarese · 2013
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Activitynet: A large-scale video benchmark for human activity understanding
Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles · 2015
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Efficient model learning from joint-action demonstrations for human-robot collaborative tasks
Stefanos Nikolaidis, Ramya Ramakrishnan, Keren Gu, and Julie Shah · 2015
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Joint inference of groups, events and human roles in aerial videos
Tianmin Shu, Dan Xie, Brandon Rothrock, Sinisa Todorovic, and Song Chun Zhu · 2015
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Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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A hierarchical deep temporal model for group activity recognition
Mostafa S Ibrahim, Srikanth Muralidharan, Zhiwei Deng, Arash Vahdat, and Greg Mori · 2016
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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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
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Learning physical collaborative robot behaviors from human demonstrations
Leonel Rozo, Sylvain Calinon, Darwin G Caldwell, Pablo Jimenez, and Carme Torras · 2016
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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Chris L Baker, Julian Jara-Ettinger, Rebecca Saxe, and Joshua B Tenenbaum · 2017
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Home: a household multimodal environment
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, and Aaron C. Courville · 2017
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Pommerman: A multi-agent playground
Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna · 2018
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M 3 rl: Mind-aware multi-agent management reinforcement learning
Tianmin Shu and Yuandong Tian · 2018
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Charades-ego: A large-scale dataset of paired third and first person videos
Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari · 2018
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Building generalizable agents with a realistic and rich 3d environment
Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian · 2018
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Gibson env: Real-world perception for embodied agents
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IQA: visual question answering in interactive environments
Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi · 2017
Cited alongside, same era.
AI2-THOR: An Interactive 3D Environment for Visual AI
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
Tianmin Shu, Caiming Xiong, and Richard Socher · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Visual semantic planning using deep successor representations
Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, and Ali Farhadi · 2017
Cited alongside, same era.
Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V Albrecht and Peter Stone · 2018
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Fei Xia, Amir R Zamir, Zhiyang He, Alexander Sax, Jitendra Malik, and Silvio Savarese · 2018
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Emergent tool use from multi-agent autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, and Igor Mordatch · 2019
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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
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Vrkitchen: an interactive 3d virtual environment for task-oriented learning
Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, and Song-Chun Zhu · 2019
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Evaluating fluency in human–robot collaboration
Guy Hoffman · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castañeda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, and Thore Graepel · 2019
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Synthesizing environment-aware activities via activity sketches
Yuan-Hong Liao, Xavier Puig, Marko Boben, Antonio Torralba, and Sanja Fidler · 2019
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The starcraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Phil ip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
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Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al · 2019
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox · 2019
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Theory of minds: Understanding behavior in groups through inverse planning
Michael Shum, Max Kleiman-Weiner, Michael L Littman, and Joshua B Tenenbaum · 2019
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Neural mmo: A massively multiagent game environment for training and evaluating intelligent agents
Joseph Suarez, Yilun Du, Phillip Isola, and Igor Mordatch · 2019
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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
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Threedworld: A platform for interactive multi-modal physical simulation, 2020
Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund, David Cox, James J. DiCarlo, Josh McDermott, Joshua B. Tenenbaum, and Daniel L. K. Yamins · 2020
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