M. Ogrodniczuk and A. Przepiórkowski, Linguistic Processing Chains as Web Services: Initial Linguistic Considerations, Proceedings of the Workshop on Web Services and Processing Pipelines in HLT: Tool Evaluation, LR Production and Validation (WSPP 2010) at the Language Resources and Evaluation Conference, pp.1-7, 2010.

I. Bratko and D. Suc, Learning qualitative models, Artificial Intelligence, vol.24, issue.4, p.107, 2003.

M. P. Fromherz, D. G. Bobrow, and J. De-kleer, Model-based computing for design and control of reconfigurable systems, AI magazine, vol.24, issue.4, p.120, 2003.

F. Rodrigues, N. Oliveira, and L. Barbosa, Towards an engine for coordination-based architectural reconfigurations, Computer Science and Information Systems, vol.12, issue.2, pp.607-634, 2015.
DOI : 10.2298/CSIS140912019R

URL : http://doi.org/10.2298/csis140912019r

J. Doucy, H. Abdulrab, P. Giroux, J. Kotowicz, W. B. Knox et al., Méthodologie pour l'orchestration sémantique de services dans le domaine de la fouille de documents multimédia Framing reinforcement learning from human reward: Reward positivity, temporal discounting, episodicity, and performance, Artificial Intelligence, vol.225, pp.24-50, 2008.

R. Loftin, B. Peng, J. Macglashan, M. L. Littman, M. E. Taylor et al., Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning, Autonomous Agents and Multi-Agent Systems, vol.4, issue.4, pp.30-59
DOI : 10.1007/s10458-015-9283-7

A. Azaria, Z. Rabinovich, S. Kraus, C. V. Goldman, and Y. Gal, Strategic advice provision in repeated human-agent interactions, Autonomous Agents and Multi-Agent Systems, vol.5, issue.4, p.20742, 2012.
DOI : 10.1007/s10458-015-9284-6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.414

A. B. Karami, K. Sehaba, and B. Encelle, Apprentissage de connaissances d'adaptationàadaptation`adaptationà partir des feedbacks des utilisateurs, 25es Journées francophones d'Ingénierie des Connaissances, pp.125-136, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01015966

R. Akrour, M. Schoenauer, and M. Sebag, Preference-Based Policy Learning, Machine Learning and Knowledge Discovery in Databases, pp.12-27, 2011.
DOI : 10.1007/978-3-642-23780-5_11

URL : https://hal.archives-ouvertes.fr/inria-00625001

. Weblab, WebLab wiki, pp.2015-2018, 2015.

. Tika, Apache Tika -a content analysis toolkit, pp.2015-2017, 2015.

. Ngramj, NGramJ, smart scanning for document properties, pp.2015-2017, 2015.

L. Serrano, Vers une capitalisation des connaissances orientée utilisateur: extraction et structuration automatiques de l'information issue de sources ouvertes, 2014.

W. R. Van-hage, V. Malaisé, R. Segers, L. Hollink, and G. Schreiber, Design and use of the Simple Event Model (SEM), Web Semantics: Science, Services and Agents on the World Wide Web, pp.128-136, 2011.
DOI : 10.1016/j.websem.2011.03.003

M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming 1st, 1994.
DOI : 10.1002/9780470316887

R. J. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

C. Szepesvári, Algorithms for Reinforcement Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.4, issue.1, pp.1-103, 2010.
DOI : 10.2200/S00268ED1V01Y201005AIM009

C. J. Watkins, Learning From Delayed Rewards, Ph.D. dissertation , Kings College, 1989.

H. Gilbert, B. Zanuttini, P. Viappiani, P. Weng, and E. Nicart, Modelfree reinforcement learning with skew-symmetric bilinear utilities, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01356085

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

K. Rao and S. Whiteson, V-MAX: A General Polynomial Time Algorithm for Probably Approximately Correct Reinforcement Learning, 2011.

R. Busa-fekete, B. Szörényi, P. Weng, W. Cheng, and E. Hüllermeier, Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm, Machine Learning, pp.327-351, 2014.
DOI : 10.1007/s10994-014-5458-8

URL : https://hal.archives-ouvertes.fr/hal-01079370

P. C. Fishburn, SSB Utility theory: an economic perspective, Mathematical Social Sciences, vol.8, issue.1, pp.63-94, 1984.
DOI : 10.1016/0165-4896(84)90061-1

G. Lafree, The Global Terrorism Database: Accomplishments and Challenges | LaFree | Perspectives on Terrorism, Perspectives on Terror, vol.4, issue.1, 2010.

R. Ginstrom, The GITS Blog: Fuzzy substring matching with Levenshtein distance in Python, pp.2014-2022, 2007.