Yossiri Adulyasak, Pradeep Varakantham, Asrar Ahmed & Patrick Jaillet (2015):
Solving uncertain MDPs with objectives that are separable over instantiations of model uncertainty.
In: AAAI Conference on Artificial Intelligence,
pp. 3454–3460.
Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak & Patrick Jaillet (2013):
Regret based robust solutions for uncertain Markov decision processes.
In: Advances in Neural Information Processing Systems (NeurIPS),
pp. 881–889,
doi:10.1613/jai.5242.
Asrar Ahmed, Pradeep Varakantham, Meghna Lowalekar, Yossiri Adulyasak & Patrick Jaillet (2017):
Sampling based approaches for minimizing regret in uncertain Markov decision processes (MDPs).
Journal of Artificial Intelligence Research (JAIR) 59,
pp. 229–264,
doi:10.1613/jair.5242.
Sule Anjomshoae, Amro Najjar, Davide Calvaresi & Kary Främling (2019):
Explainable agents and robots: Results from a systematic literature review.
In: International Conference on Autonomous Agents and MultiAgent Systems (AAMAS),
pp. 1078–1088.
J. Andrew Bagnell, Andrew Y. Ng & Jeff G. Schneider (2001):
Solving uncertain Markov decision processes.
Technical Report.
Carnegie Mellon University.
Connor Basich, Justin Svegliato, Kyle Hollins Wray, Stefan Witwicki, Joydeep Biswas & Shlomo Zilberstein (2020):
Learning to Optimize Autonomy in Competence-Aware Systems.
In: International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS),
pp. 123–131.
Felix Berkenkamp, Matteo Turchetta, Angela Schoellig & Andreas Krause (2017):
Safe model-based reinforcement learning with stability guarantees.
In: Advances in Neural Information Processing Systems (NeurIPS),
pp. 908–918.
Alberto Broggi, Massimo Bertozzi, Alessandra Fascioli, C. Guarino Lo Bianco & Aurelio Piazzi (1999):
The ARGO autonomous vehicle’s vision and control systems.
International Journal of Intelligent Control and Systems 3(4),
pp. 409–441.
Alberto Broggi, Pietro Cerri, Mirko Felisa, Maria Chiara Laghi, Luca Mazzei & Pier Paolo Porta (2012):
The VisLab Intercontinental Autonomous Challenge: An extensive test for a platoon of intelligent vehicles.
International Journal of Vehicle Autonomous Systems 10(3),
pp. 147–164,
doi:10.1504/IJVAS.2012.051250.
Katherine Chen & Michael Bowling (2012):
Tractable objectives for robust policy optimization.
In: Advances in Neural Information Processing Systems (NeurIPS),
pp. 2069–2077.
Sonia Chernova & Manuela Veloso (2009):
Interactive policy learning through confidence-based autonomy.
Journal of Artificial Intelligence Research (JAIR) 34,
pp. 1–25,
doi:10.1613/jair.2584.
Jeffery A. Clouse (1996):
On integrating apprentice learning and reinforcement learning.
University of Massachusetts Amherst.
Manoranjan Dash & Huan Liu (1997):
Feature selection for classification.
Intelligent Data Analysis 1(3),
pp. 131–156,
doi:10.1016/S1088-467X(97)00008-5.
Francesco Del Duchetto, Ayse Kucukyilmaz, Luca Iocchi & Marc Hanheide (2018):
Do not make the same mistakes again and again: Learning local recovery policies for navigation from human demonstrations.
IEEE Robotics and Automation Letters (RA-L) 3(4),
pp. 4084–4091,
doi:10.1109/LRA.2018.2861080.
Karina V. Delgado, Leliane N. De Barros, Daniel B. Dias & Scott Sanner (2016):
Real-time dynamic programming for Markov decision processes with imprecise probabilities.
Artificial Intelligence (AIJ) 230,
pp. 192–223,
doi:10.1016/j.artint.2015.09.005.
Karina Valdivia Delgado, Scott Sanner & Leliane Nunes De Barros (2011):
Efficient solutions to factored MDPs with imprecise transition probabilities.
Artificial Intelligence (AIJ) 175(9-10),
pp. 1498–1527,
doi:10.1016/j.artint.2011.01.001.
Ernst D. Dickmanns (2007):
Dynamic vision for perception and control of motion.
Springer Science & Business Media,
doi:10.1007/978-1-84628-638-4.
Carlos Diuk, Lihong Li & Bethany R. Leffler (2009):
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning.
In: International Conference on Machine Learning (ICML),
pp. 249–256,
doi:10.1145/1553374.1553406.
Liang Du & Yi-Dong Shen (2015):
Unsupervised feature selection with adaptive structure learning.
In: ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD),
pp. 209–218,
doi:10.1145/2783258.2783345.
Yang Gao & Steve Chien (2017):
Review on space robotics: Toward top-level science through space exploration.
Science Robotics 2(7),
doi:10.1126/scirobotics.aan5074.
Nick Hawes, Christopher Burbridge, Ferdian Jovan, Lars Kunze, Bruno Lacerda, Lenka Mudrova, Jay Young, Jeremy Wyatt, Denise Hebesberger & Tobias Kortner (2017):
The STRANDS project: Long-term autonomy in everyday environments.
IEEE Robotics & Automation Magazine 24(3),
doi:10.1109/MRA.2016.2636359.
Kevin Anthony Hoff & Masooda Bashir (2015):
Trust in automation: Integrating empirical evidence on factors that influence trust.
Human Factors 57(3),
pp. 407–434,
doi:10.1177/0018720814547570.
Yin Lou, Rich Caruana, Johannes Gehrke & Giles Hooker (2013):
Accurate intelligible models with pairwise interactions.
In: ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD),
pp. 623–631,
doi:10.1145/2487575.2487579.
Wim Meeussen, Eitan Marder-Eppstein, Kevin Watts & Brian P. Gerkey (2011):
Long term autonomy in office environments.
In: Robotics: Science and Systems (RSS) ALONE Workshop.
John F. Mustard, D. Beaty & D. Bass (2013):
Mars 2020 science rover: Science goals and mission concept.
In: AAS/Division for Planetary Sciences Meeting Abstracts 45.
Feiping Nie, Wei Zhu & Xuelong Li (2016):
Unsupervised feature selection with structured graph optimization.
In: AAAI Conference on Artificial Intelligence,
pp. 1302–1308.
Luis Pineda, Takeshi Takahashi, Hee-Tae Jung, Shlomo Zilberstein & Roderic Grupen (2015):
Continual planning for search and rescue robots.
In: IEEE/RAS International Conference on Humanoid Robots (Humanoids).
IEEE,
pp. 243–248,
doi:10.1109/HUMANOIDS.2015.7363542.
Kevin Regan & Craig Boutilier (2010):
Robust policy computation in reward-uncertain MDPs using nondominated policies.
In: AAAI Conference on Artificial Intelligence,
pp. 1127–1133.
Kevin Regan & Craig Boutilier (2011):
Robust online optimization of reward-uncertain MDPs.
In: International Joint Conference on Artificial Intelligence (IJCAI),
pp. 2165–2171,
doi:10.5591/978-1-57735-516-8/IJCAI11-361.
Marc Rigter, Bruno Lacerda & Nick Hawes (2020):
A Framework for learning From demonstration with minimal human effort.
IEEE Robotics and Automation Letters (RA-L) 5(2),
pp. 2023–2030,
doi:10.1109/LRA.2018.2861080.
Michael T. Rosenstein & Andrew G. Barto (2004):
Supervised actor-critic reinforcement learning.
In: J. Si, A. G. Barto, W. B. Powell & D. Wunsch: Handbook of Learning and Approximate Dynamic Programming, chapter 7.
IEEE Press,
pp. 359–380.
Sandhya Saisubramanian, Shlomo Zilberstein & Prashant Shenoy (2017):
Optimizing electric vehicle charging through determinization.
In: ICAPS Workshop on Scheduling and Planning Applications.
Jay K. Satia & Roy E. Lave Jr. (1973):
Markovian decision processes with uncertain transition probabilities.
Operations Research 21(3),
pp. 728–740,
doi:10.1287/opre.21.3.728.
Ricardo Shirota Filho, Fabio Gagliardi Cozman, Felipe W. Trevizan, Cassio Polpo de Campos & Leliane Nunes De Barros (2007):
Multilinear and integer programming for Markov decision processes with imprecise probabilities.
In: 5th International Symposium on Imprecise Probability: Theories and Applications,
pp. 395–404.
William D. Smart & Leslie Pack Kaelbling (2002):
Effective reinforcement learning for mobile robots.
In: IEEE International Conference on Robotics and Automation (ICRA) 4,
pp. 3404–3410,
doi:10.1109/ROBOT.2002.1014237.
Halit Bener Suay & Sonia Chernova (2011):
Effect of human guidance and state space size on interactive reinforcement learning.
In: IEEE Conference on Robot and Human Interactive Communication (RO-MAN).
IEEE,
pp. 1–6,
doi:10.1109/ROMAN.2011.6005223.
Justin Svegliato, Kyle Hollins Wray, Stefan J. Witwicki, Joydeep Biswas & Shlomo Zilberstein (2019):
Belief Space Metareasoning for Exception Recovery.
In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
pp. 1224–1229,
doi:10.1109/IROS40897.2019.8967676.
Lisa Torrey & Matthew Taylor (2013):
Teaching on a budget: Agents advising agents in reinforcement learning.
In: International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS),
pp. 1053–1060.
Felipe W. Trevizan, Fabio Gagliardi Cozman & Leliane Nunes de Barros (2007):
Planning under risk and knightian uncertainty.
In: International Joint Conference on Artificial Intelligence (IJCAI) 2007,
pp. 2023–2028.
Thomas J. Walsh, Sergiu Goschin & Michael L. Littman (2010):
Integrating sample-based planning and model-based reinforcement learning.
In: AAAI Conference on Artificial Intelligence,
pp. 612–617.
Chelsea C. White III & Hany K. El-Deib (1986):
Parameter imprecision in finite state, finite action dynamic programs.
Operations Research 34(1),
pp. 120–129,
doi:10.1287/opre.34.1.120.
Chelsea C. White III & Hany K. Eldeib (1994):
Markov decision processes with imprecise transition probabilities.
Operations Research 42(4),
pp. 739–749,
doi:10.1287/opre.42.4.739.
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M. Rehg, Byron Boots & Evangelos A. Theodorou (2017):
Information theoretic MPC for model-based reinforcement learning.
In: IEEE International Conference on Robotics and Automation (ICRA).
IEEE,
pp. 1714–1721,
doi:10.1109/ICRA.2017.7989202.
Eric M. Wolff, Ufuk Topcu & Richard M. Murray (2012):
Robust control of uncertain Markov decision processes with temporal logic specifications.
In: Conference on Decision and Control (CDC).
IEEE,
pp. 3372–3379,
doi:10.1109/CDC.2012.6426174.
Kyle Hollins Wray, Luis Enrique Pineda & Shlomo Zilberstein (2016):
Hierarchical Approach to Transfer of Control in Semi-Autonomous Systems.
In: International Joint Conference on Artificial Intelligence (IJCAI),
pp. 517–523.
Huan Xu & Shie Mannor (2009):
Parametric regret in uncertain Markov decision processes.
In: Conference on Decision and Control (CDC).
IEEE,
pp. 3606–3613,
doi:10.1109/CDC.2009.5400796.
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