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Seamlessly interacting with humans or robots is hard because these agents are non-stationary.
Models of bounded rationality: Empirically grounded economic reason , volume 3
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Modeling bounded rationality
A. Rubinstein and C.-j. Dalgaard · 1998
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Planning and acting in partially observable stochastic domains
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Vision-based control of an air hockey playing robot
B. E. Bishop and M. W. Spong · 1999
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Defining and using ideal teammate and opponent agent models: A case study in robotic soccer
P. Stone, P. Riley, and M. Veloso · 2000
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Humanoid robot learning and game playing using pc-based vision
D. C. Bentivegna, A. Ude, C. G. Atkeson, and G. Cheng · 2002
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Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation
R. Vidal, O. Shakernia, H. J. Kim, D. H. Shim, and S. Sastry · 2002
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Understanding human intentions via hidden markov models in autonomous mobile robots
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Multi-agent learning with policy prediction
C. Zhang and V. Lesser · 2010
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Bayesian theory of mind: Modeling joint belief-desire attribution
C. Baker, R. Saxe, and J. Tenenbaum · 2011
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Development of air hockey robot improving with the human players
M. Ogawa, S. Shimizu, T. Kadogawa, T. Hashizume, S. Kudoh, T. Suehiro, Y. Sato, and K. Ikeuchi · 2011
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Intention-aware motion planning
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Probabilistic movement modeling for intention inference in human–robot interaction
Z. Wang, K. Mülling, M. P. Deisenroth, H. Ben Amor, D. Vogt, B. Schölkopf, and J. Peters · 2013
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Legibility and predictability of robot motion
A. D. Dragan, K. C. Lee, and S. S. Srinivasa · 2013
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Hierarchical processing architecture for an air-hockey robot system
A. Namiki, S. Matsushita, T. Ozeki, and K. Nonami · 2013
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Opponent modeling by expectation–maximization and sequence prediction in simplified poker
R. Mealing and J. L. Shapiro · 2015
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E-hba: Using action policies for expert advice and agent typification
S. V. Albrecht, J. W. Crandall, and S. Ramamoorthy · 2015
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Intention-aware online pomdp planning for autonomous driving in a crowd
H. Bai, S. Cai, N. Ye, D. Hsu, and W. S. Lee · 2015
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Learning to communicate with deep multi-agent reinforcement learning
J. Foerster, I. A. Assael, N. De Freitas, and S. Whiteson · 2016
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Opponent modeling in deep reinforcement learning
H. He, J. Boyd-Graber, K. Kwok, and H. Daumé III · 2016
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Information gathering actions over human internal state
D. Sadigh, S. S. Sastry, S. A. Seshia, and A. Dragan · 2016
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Multi-agent cooperation and the emergence of (natural) language
A. Lazaridou, A. Peysakhovich, and M. Baroni · 2016
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Learning multiagent communication with backpropagation
Modeling others using oneself in multi-agent reinforcement learning
R. Raileanu, E. Denton, A. Szlam, and R. Fergus · 2018
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N. C. Rabinowitz, F. Perbet, H. F. Song, C. Zhang, S. Eslami, and M. Botvinick · 2018
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Emergence of grounded compositional language in multi-agent populations
I. Mordatch and P. Abbeel · 2018
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Planning with trust for human-robot collaboration
M. Chen, S. Nikolaidis, H. Soh, D. Hsu, and S. Srinivasa · 2018
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Planning for cars that coordinate with people: Leveraging effects on human actions for planning and active information gathering over human internal state
D. Sadigh, N. Landolfi, S. S. Sastry, S. A. Seshia, and A. D. Dragan · 2018
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S. Sukhbaatar, R. Fergus, et al · 2016
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Planning for autonomous cars that leverage effects on human actions
D. Sadigh, S. Sastry, S. A. Seshia, and A. D. Dragan · 2016
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Implicitly assisting humans to choose good grasps in robot to human handovers
A. Bestick, R. Bajcsy, and A. D. Dragan · 2016
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Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations
F. Doshi-Velez and G. Konidaris · 2016
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Multi-agent actor-critic for mixed cooperative-competitive environments
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. P. Abbeel, and I. Mordatch · 2017
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Efficiently detecting switches against non-stationary opponents
P. Hernandez-Leal, Y. Zhan, M. E. Taylor, L. E. Sucar, and E. M. De Cote · 2017
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Deep variational reinforcement learning for pomdps
M. Igl, L. Zintgraf, T. A. Le, F. Wood, and S. Whiteson · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Active learning of reward dynamics from hierarchical queries
C. Basu, E. Biyik, Z. He, M. Singhal, and D. Sadigh · 2019
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Robots that take advantage of human trust
D. P. Losey and D. Sadigh · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
N. Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. Z. Leibo, and N. De Freitas · 2019
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Influencing leading and following in human-robot teams
M. Kwon, M. Li, A. Bucquet, and D. Sadigh · 2019
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Learning latent dynamics for planning from pixels
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
A. X. Lee, A. Nagabandi, P. Abbeel, and S. Levine · 2019
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Learning from my partner’s actions: Roles in decentralized robot teams
D. P. Losey, M. Li, J. Bohg, and D. Sadigh · 2020
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Variational autoencoders for opponent modeling in multi-agent systems
G. Papoudakis and S. V. Albrecht · 2020
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Learning to interactively learn and assist
M. Woodward, C. Finn, and K. Hausman · 2020
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Deep reinforcement learning amidst lifelong non-stationarity
A. Xie, J. Harrison, and C. Finn · 2020
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