A. Agostini, C. Torras, and F. Wörgötter, Efficient interactive decision-making framework for robotic applications, Artificial Intelligence, vol.247, pp.187-212, 2017.
DOI : 10.1016/j.artint.2015.04.004

URL : http://www.iri.upc.edu/files/scidoc/1648-Efficient-Interactive-Decision-making-Framework-for-Robotic-Applications.pdf

E. Amir and A. Chang, Learning partially observable deterministic action models, Journal of Artificial Intelligence Research, vol.33, pp.349-402, 2008.

I. Ronen, M. Brafman, and . Tennenholtz, R-max -A general polynomial time algorithm for near-optimal reinforcement learning, Journal of Machine Learning Research, vol.3, pp.213-231, 2003.

S. Chernova and M. Veloso, Interactive policy learning through confidence-based autonomy, Journal of Artificial Intelligence Research, vol.34, issue.1, pp.1-25, 2009.

A. Deshpande, B. Milch, S. Luke, L. P. Zettlemoyer, and . Kaelbling, Learning probabilistic relational dynamics for multiple tasks, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp.83-92, 2007.

C. Diuk, A. Cohen, L. Michael, and . Littman, An object-oriented representation for efficient reinforcement learning, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.240-247, 2008.
DOI : 10.1145/1390156.1390187

URL : https://rucore.libraries.rutgers.edu/rutgers-lib/30035/PDF/1/

S. D?eroski, L. De-raedt, and K. Driessens, Relational reinforcement learning, Machine Learning, pp.7-52, 2001.
DOI : 10.1007/BFb0027307

F. Esposito, S. Ferilli, N. Fanizzi, T. M. , A. Basile et al., Incremental learning and concept drift in INTHELEX, Intelligent Data Analysis, vol.8, issue.3, pp.213-237, 2004.

H. Daniel, O. Grollman, and . Jenkins, Dogged learning for robots, Proceedings of the International Conference on Robotics and Automation, pp.2483-2488, 2007.

M. Helmert, The fast downward planning system, Journal of Artificial Intelligence Research, vol.26, pp.191-246, 2006.

T. Hester and P. Stone, TEXPLORE: real-time sample-efficient reinforcement learning for robots, Machine Learning, pp.385-429, 2013.
DOI : 10.1109/18.382012

W. Hoeffding, Probability Inequalities for Sums of Bounded Random Variables, Journal of the American Statistical Association, vol.1, issue.301, pp.13-30, 1963.
DOI : 10.1007/BF02883985

J. Hoffmann and B. Nebel, The FF planning system: Fast plan generation through heuristic search, Journal of Artificial Intelligence Research, pp.253-302, 2001.

K. Inoue, T. Ribeiro, and C. Sakama, Learning from interpretation transition, Machine Learning, pp.51-79, 2014.
DOI : 10.1145/321978.321991

URL : https://hal.archives-ouvertes.fr/hal-01710486

S. Jiménez, F. Fernández, and D. Borrajo, The PELA architecture: integrating planning and learning to improve execution, Proceedings of the AAAI Conference on Artificial Intelligence, pp.1294-1299, 2008.

M. Kearns and S. Singh, Near-optimal reinforcement learning in polynomial time, Machine Learning, pp.209-232, 2002.

T. Keller and P. Eyerich, PROST: Probabilistic Planning Based on UCT, Proceedings of the International Conference on Automated Planning and Scheduling, pp.119-127, 2012.

W. Bradley, K. , and P. Stone, Reinforcement learning from simultaneous human and MDP reward, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, pp.475-482, 2012.

A. Kolobov, P. Dai, . Mausam, S. Daniel, and . Weld, Reverse iterative deepening for finite-horizon MDPs with large branching factors, Proceedings of the International Conference on Automated Planning and Scheduling, pp.146-154, 2012.

G. Konidaris, I. Scheidwasser, G. Andrew, and . Barto, Transfer in reinforcement learning via shared features, The Journal of Machine Learning Research, vol.13, issue.1, pp.1333-1371, 2012.

J. Kulick, M. Toussaint, T. Lang, and M. Lopes, Active learning for teaching a robot grounded relational symbols, Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pp.1451-1457, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00871858

T. Lang and M. Toussaint, Planning with noisy probabilistic relational rules, Journal of Artificial Intelligence Research, vol.39, pp.1-49, 2010.

T. Lang, M. Toussaint, and K. Kersting, Exploration in relational domains for model-based reinforcement learning, Journal of Machine Learning Research, vol.13, pp.3691-3734, 2012.

L. Li, L. Michael, . Littman, J. Thomas, A. L. Walsh et al., Knows what it knows, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.399-443, 2011.
DOI : 10.1145/1390156.1390228

I. Little and S. Thiebaux, Probabilistic planning vs. replanning, Proceedings of the ICAPS Workshop on IPC: Past, Present and Future, 2007.

L. Michael and . Littman, Reinforcement learning improves behaviour from evaluative feedback, Nature, vol.521, issue.7553, pp.445-451, 2015.

D. Martínez, G. Alenyà, and C. Torras, Planning robot manipulation to clean planar surfaces, Engineering Applications of Artificial Intelligence, vol.39, pp.23-32, 2015.
DOI : 10.1016/j.engappai.2014.11.004

D. Martínez, G. Alenyà, and C. , Efficient reinforcement learning through demonstrations and relaxed reward demands, Proceedings of The AAAI Conference on Artificial Intelligence, pp.2857-2863, 2015.

D. Martínez, T. Ribeiro, K. Inoue, G. Alenyà, and C. Torras, Learning probabilistic action models from interpretation transitions, Technical Communication of the International Conference on Logic Programming, CEUR Workshop Proceedings, 2015.

D. Martínez, G. Alenyà, C. Torras, T. Ribeiro, and K. Inoue, Learning relational dynamics of stochastic domains for planning, International Conference on Automated Planning and Scheduling, pp.235-243, 2016.

C. ¸-etin-meriçli, M. Veloso, and H. Levent-ak?n, Multi-resolution Corrective Demonstration for Efficient Task Execution and Refinement, International Journal of Social Robotics, vol.32, issue.2, pp.423-435, 2012.
DOI : 10.1007/s10514-012-9284-1

B. Moldovan, P. Moreno, M. Van-otterlo, J. Santos-victor, and L. De-raedt, Learning relational affordance models for robots in multi-object manipulation tasks, 2012 IEEE International Conference on Robotics and Automation, pp.4373-4378, 2012.
DOI : 10.1109/ICRA.2012.6225042

M. Molineaux, W. David, and . Aha, Learning unknown event models, Proc. of the AAAI Conference on Artificial Intelligence, pp.395-401, 2014.

K. Mourão, Learning probabilistic planning operators from noisy observations, Proceedings of the Workshop of the UK Planning and Scheduling Special Interest Group, 2014.

K. Mourão, S. Luke, R. Zettlemoyer, M. Petrick, and . Steedman, Learning STRIPS operators from noisy and incomplete observations, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp.614-623, 2012.

M. Hanna, . Pasula, S. Luke, L. P. Zettlemoyer, and . Kaelbling, Learning symbolic models of stochastic domains, Journal of Artificial Intelligence Research, vol.29, issue.1, pp.309-352, 2007.

T. Ribeiro and K. Inoue, Learning Prime Implicant Conditions from Interpretation Transition, Proceedings of the International Conference on Inductive Logic Programming , LNAI, pp.108-125, 2014.
DOI : 10.1145/321978.321991

URL : https://hal.archives-ouvertes.fr/hal-01710486

T. Ribeiro, M. Magnin, K. Inoue, and C. Sakama, Learning Multi-valued Biological Models with Delayed Influence from Time-Series Observations, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp.25-31, 2015.
DOI : 10.1109/ICMLA.2015.19

S. Sanner, Relational dynamic influence diagram language (RDDL): Language description . Unpublished ms, 2010.

D. Sykes, D. Corapi, J. Magee, J. Kramer, A. Russo et al., Learning revised models for planning in adaptive systems, 2013 35th International Conference on Software Engineering (ICSE), pp.63-71, 2013.
DOI : 10.1109/ICSE.2013.6606552

E. Matthew, P. Taylor, and . Stone, Transfer learning for reinforcement learning domains: A survey, Journal of Machine Learning Research, vol.10, pp.1633-1685, 2009.

I. Thon, N. Landwehr, and L. De-raedt, Stochastic relational processes: Efficient inference and??applications, Machine Learning, pp.239-272, 2011.
DOI : 10.1007/11853886_37

URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-010-5213-8.pdf

M. Vallati, L. Chrpa, M. Grze´sgrze´s, L. Thomas, M. Mccluskey et al., The 2014 International Planning Competition: Progress and Trends, AI Magazine, vol.36, issue.3, pp.90-98, 2015.
DOI : 10.1609/aimag.v36i3.2571

URL : http://kar.kent.ac.uk/53894/1/aimag15_ipc.pdf

J. Thomas and . Walsh, Efficient learning of relational models for sequential decision making, 2010.

J. Thomas, I. Walsh, C. Szita, . Diuk, L. Michael et al., Exploring compact reinforcement-learning representations with linear regression, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp.591-598, 2009.

J. Thomas, K. Walsh, . Subramanian, L. Michael, C. Littman et al., Generalizing apprenticeship learning across hypothesis classes, Proceedings of the International Conference on Machine Learning, pp.1119-1126, 2010.

J. Thomas, . Walsh, K. Daniel, . Hewlett, T. Clayton et al., Blending autonomous exploration and apprenticeship learning, Advances in Neural Information Processing Systems, pp.2258-2266, 2011.

L. Håkan, . Younes, L. Michael, and . Littman, PPDDL1. 0: An extension to PDDL for expressing planning domains with probabilistic effects, 2004.

G. Zhu, D. Lizotte, and J. Hoey, Scalable approximate policies for Markov decision process models of hospital elective admissions, Artificial Intelligence in Medicine, vol.61, issue.1, pp.21-34, 2014.
DOI : 10.1016/j.artmed.2014.04.001

H. Hankui, Z. , and S. Kambhampati, Action-model acquisition from noisy plan traces, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp.2444-2450, 2013.

H. Hankui, Z. , and Q. Yang, Action-model acquisition for planning via transfer learning, Artificial Intelligence, vol.212, pp.80-103, 2014.