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Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.
The algorithmic automation problem: Prediction, triage, and human effort
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Instant messaging and interruption: Influence of task type on performance
Mary Czerwinski, Edward Cutrell, and Eric Horvitz · 2000
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Exploring the relations between categorization and decision making with regard to realistic face stimuli
James T. Townsend, Kam M. Silva, Jesse Spencer-Smith, and Michael J. Wenger · 2000
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Behavioural impacts of advanced driver assistance systems–an overview
K. Brookhuis, D. De Waard, and W. Janssen · 2001
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Learning and reasoning about interruption
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Understanding and modeling the human driver
C. Macadam · 2003
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Regulation (EC) No 561/2006
European Parliament · 2006
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Understanding and developing models for detecting and differentiating breakpoints during interactive tasks
Shamsi T Iqbal and Brian P Bailey · 2007
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Classification with a reject option using a hinge loss
P. Bartlett and M. Wegkamp · 2008
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An analysis of model-based interval estimation for markov decision processes
A. Strehl and M. Littman · 2008
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Near-optimal regret bounds for reinforcement learning
T. Jaksch, R. Ortner, and P. Auer · 2010
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Ad hoc autonomous agent teams: Collaboration without pre-coordination
Peter Stone, Gal A Kaminka, Sarit Kraus, and Jeffrey S Rosenschein · 2010
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Integrating reinforcement learning with human demonstrations of varying ability
Matthew E Taylor, Halit Bener Suay, and Sonia Chernova · 2011
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Blending autonomous exploration and apprenticeship learning
Thomas J Walsh, Daniel K Hewlett, and Clayton T Morrison · 2011
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An analysis framework for ad hoc teamwork tasks
Samuel Barrett and Peter Stone · 2012
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Pomcop: Belief space planning for sidekicks in cooperative games
O. Macindoe, L. Kaelbling, and T. Lozano-Pérez · 2012
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(more) efficient reinforcement learning via posterior sampling
Ian Osband, Daniel Russo, and Benjamin Van Roy · 2013
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Teaching on a budget: Agents advising agents in reinforcement learning
Lisa Torrey and Matthew Taylor · 2013
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The algorithmic anatomy of model-based evaluation
Nathaniel D. Daw and Peter Dayan · 2014
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Near-optimal reinforcement learning in factored mdps
Ian Osband and Benjamin Van Roy · 2014
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Thompson sampling for learning parameterized markov decision processes
Aditya Gopalan and Shie Mannor · 2015
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Human intent prediction using markov decision processes
Catharine L. R. McGhan, Ali Nasir, and Ella M. Atkins · 2015
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Human-level control through deep reinforcement learning
V. Mnih et al · 2015
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Efficient model learning from joint-action demonstrations for human-robot collaborative tasks
S. Nikolaidis, R. Ramakrishnan, K. Gu, and J. Shah · 2015
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Learning with rejection
C. Cortes, G. DeSalvo, and M. Mohri · 2016
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Machine teaching for inverse reinforcement learning: Algorithms and applications
Daniel S Brown and Scott Niekum · 2019
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Selectivenet: A deep neural network with an integrated reject option
Y. Geifman and R. El-Yaniv · 2019
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Interrupted by my car? implications of interruption and interleaving research for automated vehicles
Christian P Janssen, Shamsi T Iqbal, Andrew L Kun, and Stella F Donker · 2019
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Interactive teaching algorithms for inverse reinforcement learning
Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, and Adish Singla · 2019
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Deep gamblers: Learning to abstain with portfolio theory
Z. Liu, Z. Wang, P. Liang, R. Salakhutdinov, L. Morency, and M. Ueda · 2019
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Cooperative inverse reinforcement learning
D. Hadfield-Menell, S. Russell, P. Abbeel, and A. Dragan · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver et al · 2016
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Mathematical models of adaptation in human-robot collaboration
S. Nikolaidis, J. Forlizzi, D. Hsu, J. Shah, and S. Srinivasa · 2017
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Mastering the game of go without human knowledge
D. Silver et al · 2017
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Learning against non-stationary agents with opponent modelling and deep reinforcement learning
R. Everett and S. Roberts · 2018
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Bias-reduced uncertainty estimation for deep neural classifiers
Y. Geifman, G. Uziel, and R. El-Yaniv · 2018
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Adjustable autonomy: a systematic literature review
Salama A Mostafa, Mohd Sharifuddin Ahmad, and Aida Mustapha · 2019
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Learning to collaborate in markov decision processes
Goran Radanovic, Rati Devidze, David C. Parkes, and Adish Singla · 2019
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Combating label noise in deep learning using abstention
S. Thulasidasan, T. Bhattacharya, J. Bilmes, G. Chennupati, and J. Mohd-Yusof · 2019
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Learner-aware teaching: Inverse reinforcement learning with preferences and constraints
S. Tschiatschek, A. Ghosh, L. Haug, R. Devidze, and A. Singla · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
O. Vinyals et al · 2019
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Regression under human assistance
A. De, P. Koley, N. Ganguly, and M. Gomez-Rodriguez · 2020
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Towards deployment of robust cooperative ai agents: An algorithmic framework for learning adaptive policies
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