M. Araya-lópez, V. Thomas, O. Buffet, and F. Charpillet, A Closer Look at MOMDPs, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, pp.197-204, 2010.
DOI : 10.1109/ICTAI.2010.101

J. Attard, S. Scerri, I. Rivera, and S. Handschuh, Ontology-based situation recognition for context-aware systems, Proceedings of the 9th International Conference on Semantic Systems, I-SEMANTICS '13, pp.113-120, 2013.
DOI : 10.1145/2506182.2506197

J. C. Augusto, V. Callaghan, D. Cook, A. Kameas, and I. Satoh, "Intelligent Environments: a manifesto", Human-centric Computing and Information Sciences, vol.3, issue.1, p.12, 2013.
DOI : 10.1109/TSE.2003.1265525

R. Bellman, Dynamic Programming, 1957.

H. Berlink and A. H. Costa, Batch reinforcement learning for smart home energy management, Proceedings of the 24th International Conference on Artificial Intelligence. IJCAI'15, pp.2561-2567, 2015.

C. Bolchini, C. A. Curino, E. Quintarelli, F. A. Schreiber, and L. Tanca, A data-oriented survey of context models, ACM SIGMOD Record, vol.36, issue.4, pp.19-26, 2007.
DOI : 10.1145/1361348.1361353

A. Brenon, F. Portet, and M. Vacher, Preliminary Study of Adaptive Decision-Making System for Vocal Command in Smart Home, 2016 12th International Conference on Intelligent Environments (IE), pp.218-221, 2016.
DOI : 10.1109/IE.2016.49
URL : https://hal.archives-ouvertes.fr/hal-01345333

A. R. Cassandra, Pomdp for dummies. Website, consulted on 2016-05-17, 2016.

P. Chahuara, A. Fleury, F. Portet, and M. Vacher, On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic Smart Homes1, Journal of Ambient Intelligence and Smart Environments, vol.8, issue.3, pp.399-422, 2016.
DOI : 10.1007/s00779-009-0277-9

P. Chahuara, F. Portet, and M. Vacher, Context-aware decision making under uncertainty for voice-based control of smart home, Expert Systems with Applications, vol.75, pp.63-79, 2017.
DOI : 10.1016/j.eswa.2017.01.014
URL : https://hal.archives-ouvertes.fr/hal-01450843

L. Chen, C. D. Nugent, M. Mulvenna, D. Finlay, X. Hong et al., A logical framework for behaviour reasoning and assistance in a smart home, International Journal of Assistive Robotics and Mechatronics, vol.9, issue.4, pp.20-34, 2008.

H. Christensen, I. Casanueva, S. Cunningham, P. Green, and T. Hain, homeservice: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition, 4 th Workshop on Speech and Language Processing for Assistive Technologies, pp.29-34, 2013.

D. Carolis, B. Cozzolongo, and G. , C@sa: Intelligent home control and simulation, International Conference on Computational Intelligence. International Computational Intelligence Society international Conference on Computational Intelligence, pp.462-465, 2004.

A. K. Dey, Understanding and Using Context, Personal and Ubiquitous Computing, vol.5, issue.1, pp.4-7, 2001.
DOI : 10.1007/s007790170019

G. Filho and T. J. Moir, From science fiction to science fact: a smart-house interface using speech technology and a photo-realistic avatar, International Journal of Computer Applications in Technology, vol.39, issue.1/2/3, pp.32-39, 2010.
DOI : 10.1504/IJCAT.2010.034727

M. Gallissot, J. Caelen, F. Jambon, and B. Meillon, Une plateforme usage pour l???int??gration de l???informatique ambiante dans l???habitat. L???appartement Domus, Techniques et sciences informatiques, vol.32, issue.5, pp.547-574, 2013.
DOI : 10.3166/tsi.32.547-574

J. Gómez-romero, M. A. Serrano, M. A. Patricio, J. García, and J. M. Molina, Context-based scene recognition from visual data in smart homes: an Information Fusion approach, Personal and Ubiquitous Computing, vol.14, issue.1, pp.835-857, 2012.
DOI : 10.1155/ASP.2005.2341

R. Helaoui, D. Riboni, and H. Stuckenschmidt, A probabilistic ontological framework for the recognition of multilevel human activities, Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, UbiComp '13, pp.345-354, 2013.
DOI : 10.1145/2493432.2493501

G. E. Hinton, Lecture 6e ? rmsprop: Divide the gradient by a running average of its recent magnitude. Online courses, 2015.

R. A. Howard and J. E. Matheson, Influence Diagrams, Institute for Operations Research and the Management Sciences (INFORMS), pp.127-143, 1981.
DOI : 10.1287/deca.1050.0020

U. A. Ibarra, J. C. Augusto, and T. Clark, Engineering context-aware systems and applications: A survey, Journal of Systems and Software, vol.117, pp.55-83, 2016.

D. Istrate, M. Vacher, and J. Serignat, Embedded implementation of distress situation identification through sound analysis, The Journal on Information Technology in Healthcare, vol.6, pp.204-211, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00957417

A. Karami, A. Fleury, J. Boonaert, and S. Lecoeuche, User in the Loop: Adaptive Smart Homes Exploiting User Feedback???State of the Art and Future Directions, Information, vol.7524, issue.2, p.35, 2016.
DOI : 10.1007/s10458-006-0005-z
URL : https://hal.archives-ouvertes.fr/hal-01332467

A. Karpathy, Cs231n: Convolutional neural networks for visual recognition. Web site. URL https, 2015.

N. Katzouris, A. Artikis, and G. Paliouras, Event Recognition for Unobtrusive Assisted Living, pp.475-488, 2014.
DOI : 10.1007/978-3-319-07064-3_41

M. J. Kofler, C. Reinisch, and W. Kastner, A semantic representation of energy-related information in future smart homes, Energy and Buildings, vol.47, pp.169-179, 2012.
DOI : 10.1016/j.enbuild.2011.11.044

B. Lecouteux, M. Vacher, and F. Portet, Distant speech recognition in a smart home: Comparison of several multisource asrs in realistic conditions, pp.2273-2276, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00642306

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

S. Lee and S. Cho, Fusion of Modular Bayesian Networks for Context-Aware Decision Making, Hybrid Artificial Intelligent Systems, pp.375-384, 2012.
DOI : 10.1007/978-3-642-28942-2_34

C. Y. Leong, A. R. Ramli, and T. Perumal, A rule-based framework for heterogeneous subsystems management in smart home environment, IEEE Transactions on Consumer Electronics, vol.55, issue.3, pp.1208-1213, 2009.
DOI : 10.1109/TCE.2009.5277977

D. Li and S. K. Jayaweera, Reinforcement learning aided smart-home decision-making in an interactive smart grid, 2014 IEEE Green Energy and Systems Conference (IGESC), pp.1-6, 2014.
DOI : 10.1109/IGESC.2014.7018632

H. Liao and C. Tu, A rdf and owl-based temporal context reasoning model for smart home, Information Technology Journal, vol.6, issue.8, pp.1130-1138, 2007.

L. Lin, Reinforcement learning for robots using neural networks, 1993.

Z. C. Lipton, J. Berkowitz, and C. Elkan, A critical review of recurrent neural networks for sequence learning, 2015.

S. Loke, Context-Aware Pervasive Systems, 2006.
DOI : 10.1201/9781420013498

A. Mileo, D. Merico, and R. Bisiani, Reasoning support for risk prediction and prevention in independent living. Theory Pract, Log. Program, vol.11, issue.2-3, pp.361-395, 2011.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, pp.529-533, 2015.
DOI : 10.1016/S0004-3702(98)00023-X

P. Moore, B. Hu, and M. Jackson, Rule Strategies for Intelligent Context-Aware Systems: The Application of Conditional Relationships in Decision-Support, 2011 International Conference on Complex, Intelligent, and Software Intensive Systems, pp.9-16, 2011.
DOI : 10.1109/CISIS.2011.12

M. C. Mozer, The neural network house: An environment that adapts to its inhabitants, Proceedings of the American Association for Artificial Intelligence, 1998.

M. C. Mozer, Smart Environments: Technology, Protocols and Applications, Ch. 12 - Lessons from an Adaptive Home, pp.271-294, 2005.

T. Nishiyama, S. Hibiya, and T. Sawaragi, Development of agent system based on decision model for creating an ambient space, AI & SOCIETY, vol.8, issue.10, pp.247-259, 2011.
DOI : 10.5687/iscie.8.574

K. K. Peetoom, M. A. Lexis, M. Joore, C. D. Dirksen, and L. P. De-witte, Literature review on monitoring technologies and their outcomes in independently living elderly people. Disability and Rehabilitation: Assistive Technology, pp.1-24, 2014.

F. Portet, M. Vacher, C. Golanski, C. Roux, and B. Meillon, Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects, Personal and Ubiquitous Computing, pp.127-144, 2013.
DOI : 10.1038/scientificamerican0991-94
URL : https://hal.archives-ouvertes.fr/hal-00953242

A. Pritzel, B. Uria, S. Srinivasan, A. Puigdomènech, O. Vinyals et al., Neural episodic control, 2017.

N. D. Rodríguez, M. P. Cuéllar, J. Lilius, and M. D. Calvo-flores, A fuzzy ontology for semantic modelling and recognition of human behaviour, pp.46-60, 2014.

N. D. Rodríguez, M. P. Cuéllar, J. Lilius, and M. D. Calvo-flores, A survey on ontologies for human behavior recognition, ACM Computing Surveys, vol.46, issue.4, pp.1-33, 2014.
DOI : 10.1016/j.bspc.2007.09.001

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2015.
DOI : 10.1016/j.neunet.2014.09.003

M. S. Singh, V. Pondenkandath, B. Zhou, P. Lukowicz, and M. Liwicki, Transforming sensor data to the image domain for deep learning ??? An application to footstep detection, 2017 International Joint Conference on Neural Networks (IJCNN), pp.2665-2672, 2017.
DOI : 10.1109/IJCNN.2017.7966182

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd Edition. The M. I. T. Press. URL http, 2017.
DOI : 10.1007/978-1-4615-3618-5

J. Tsitsiklis and B. V. Roy, An analysis of temporal-difference learning with function approximation, IEEE Transactions on Automatic Control, vol.42, issue.5, pp.674-690, 1997.
DOI : 10.1109/9.580874

N. Usunier, G. Synnaeve, Z. Lin, and S. Chintala, Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks, 2016.

M. Vacher, S. Caffiau, F. Portet, B. Meillon, C. Roux et al., Evaluation of a Context-Aware Voice Interface for Ambient Assisted Living, ACM Transactions on Accessible Computing, vol.7, issue.2, pp.1-36, 2015.
DOI : 10.4017/gt.2009.08.03.011.00
URL : https://hal.archives-ouvertes.fr/hal-01138090

M. Vacher, B. Lecouteux, P. Chahuara, F. Portet, B. Meillon et al., The sweet-home speech and multimodal corpus for home automation interaction, The 9 th edition of the Language Resources and Evaluation Conference (LREC). Reykjavik, Iceland, pp.4499-4506, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00953006

L. J. Van-der-maaten and G. Hinton, Visualizing high-dimensional data using t-sne, Journal of Machine Learning Research, pp.2579-2605, 2008.

C. J. Watkins, Learning from delayed rewards, 1989.

M. Weiser, century, ACM SIGMOBILE Mobile Computing and Communications Review, vol.3, issue.3, pp.3-11, 1991.
DOI : 10.1145/329124.329126

K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville et al., Show, attend and tell: Neural image caption generation with visual attention, Computer Research Repository, 2015.

S. S. Yau and J. Liu, Hierarchical Situation Modeling and Reasoning for Pervasive Computing, The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06), pp.5-10, 2006.
DOI : 10.1109/SEUS-WCCIA.2006.25

O. Yilmaz, R. C. Erdur, and . Feb, iConAwa ??? An intelligent context-aware system, Expert Systems with Applications, vol.39, issue.3, pp.2907-2918, 2012.
DOI : 10.1016/j.eswa.2011.08.152

S. Zaidenberg and P. Reignier, Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant, Advances in Reinforcement Learning. Intech, pp.59-80, 2011.
DOI : 10.5772/13723
URL : https://hal.archives-ouvertes.fr/hal-00753245

S. Zaidenberg, P. Reignier, and N. Mandran, Learning User Preferences in Ubiquitous Systems: A User Study and a Reinforcement Learning Approach, Artificial Intelligence Applications, 2010.
DOI : 10.1007/978-3-642-16239-8_44
URL : https://hal.archives-ouvertes.fr/hal-00788028