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Explainable AI techniques that describe agent reward functions can enhance human-robot collaboration in a variety of settings.
An evaluation of the human-interpretability of explanation
Lage, I.; Chen, E.; He, J.; Narayanan, M.; Kim, B.; Gershman, S.; and Doshi-Velez, F. 2019a · 1902
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Explaining reinforcement learning to mere mortals: An empirical study
Anderson, A.; Dodge, J.; Sadarangani, A.; Juozapaitis, Z.; Newman, E.; Irvine, J.; Chattopadhyay, S.; Fern, A.; and Burnett, M. 2019 · 1903
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Exploring computational user models for agent policy summarization
Lage, I.; Lifschitz, D.; Doshi-Velez, F.; and Amir, O. 2019b · 1905
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Asking easy questions: A user-friendly approach to active reward learning
Bıyık, E.; Palan, M.; Landolfi, N. C.; Losey, D. P.; and Sadigh, D. 2019 · 1910
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
Kim, B.; Rudin, C.; and Shah, J. A. 2014 · 1960
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Sreedharan, S.; Soni, U.; Verma, M.; Srivastava, S.; and Kambhampati, S. 2020 · 2002
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Interactive robot training for non-markov tasks
Shah, A.; Wadhwania, S.; and Shah, J. 2020 · 2003
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A survey of robot learning from demonstration
Argall, B. D.; Chernova, S.; Veloso, M.; and Browning, B. 2009 · 2009
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Algorithmic and human teaching of sequential decision tasks
Cakmak, M.; and Lopes, M. 2012 · 2012
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Legibility and predictability of robot motion
Dragan, A. D.; Lee, K. C.; and Srinivasa, S. S. 2013 · 2013
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Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016 · 2016
Cited alongside, same era.
Cooperative inverse reinforcement learning
Hadfield-Menell, D.; Dragan, A.; Abbeel, P.; and Russell, S. 2016 · 2016
Cited alongside, same era.
” Why should i trust you?” Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
The impact of pomdp-generated explanations on trust and performance in human-robot teams
Wang, N.; Pynadath, D. V.; and Hill, S. G. 2016 · 2016
Cited alongside, same era.
Collaborative planning with encoding of users’ high-level strategies
Kim, J.; Banks, C.; and Shah, J. 2017 · 2017
Cited alongside, same era.
Bayesian inference of temporal task specifications from demonstrations
Shah, A. J.; Kamath, P.; Li, S.; and Shah, J. A. 2018 · 2018
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Plan Explanations as Model Reconciliation–An Empirical Study
Chakraborti, T.; Sreedharan, S.; Grover, S.; and Kambhampati, S. 2019 · 2019
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Enabling robots to communicate their objectives
Huang, S. H.; Held, D.; Abbeel, P.; and Dragan, A. D. 2019 · 2019
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Explainable reinforcement learning via reward decomposition
Juozapaitis, Z.; Koul, A.; Fern, A.; Erwig, M.; and Doshi-Velez, F. 2019 · 2019
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Explanation-based reward coaching to improve human performance via reinforcement learning
Tabrez, A.; Agrawal, S.; and Hayes, B. 2019 · 2019
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Pragmatic-pedagogic value alignment
Fisac, J. F.; Gates, M. A.; Hamrick, J. B.; Liu, C.; Hadfield-Menell, D.; Palaniappan, M.; Malik, D.; Sastry, S. S.; Griffiths, T. L.; and Dragan, A. D. 2020 · 2020
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Highlights: Summarizing agent behavior to people
Amir, D.; and Amir, O. 2018 · 2018
Cited alongside, same era.
Agent strategy summarization
Amir, O.; Doshi-Velez, F.; and Sarne, D. 2018 · 2018
Cited alongside, same era.
Metrics for explainable AI: Challenges and prospects
Hoffman, R. R.; Mueller, S. T.; Klein, G.; and Litman, J. 2018 · 2018
Cited alongside, same era.
Establishing appropriate trust via critical states
Huang, S. H.; Bhatia, K.; Abbeel, P.; and Dragan, A. D. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Human-in-the-Loop Learning of Interpretable and Intuitive Representations
Lage, I.; and Doshi-Velez, F. 2020 · 2020
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A Situation Awareness-Based Framework for Design and Evaluation of Explainable AI
Sanneman, L.; and Shah, J. A. 2020 · 2020
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Machine Teaching for Human Inverse Reinforcement Learning
Lee, M. S.; Admoni, H.; and Simmons, R. 2021 · 2021
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