D. L. Donoho, High-dimensional data analysis: The curses and blessings of dimensionality, In: AMS Math Challenges Lecture. p, p.132, 2000.

E. Dubrova and M. Teslenko, A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, issue.5, pp.1393-1399, 2011.
DOI : 10.1109/TCBB.2010.20

A. Garcez, A. S. Broda, K. Gabbay, and D. M. , Symbolic knowledge extraction from trained neural networks: A sound approach, Artificial Intelligence, vol.125, issue.1-2, pp.155-207, 2001.
DOI : 10.1016/S0004-3702(00)00077-1

A. Garcez, A. S. Zaverucha, and G. , The connectionist inductive learning and logic programming system, Applied Intelligence, vol.11, issue.1, pp.59-77, 1999.
DOI : 10.1023/A:1008328630915

E. Gentet, S. Tourret, and K. Inoue, Learning from interpretation transition using feed-forward neural network, Proceedings of ILP 2016, CEUR Proc. 1865, pp.27-33, 2016.

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, vol.18, issue.5-6, pp.602-610, 2005.
DOI : 10.1016/j.neunet.2005.06.042

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

K. Inoue, T. Ribeiro, and C. Sakama, Learning from interpretation transition, Machine Learning, vol.23, issue.4, pp.51-79, 2014.
DOI : 10.1145/321978.321991
URL : https://hal.archives-ouvertes.fr/hal-01710483

N. R. Jennings, K. Sycara, and M. Wooldridge, A roadmap of agent research and development, Autonomous Agents and Multi-Agent Systems, vol.1, issue.1, pp.7-381010090405266, 1998.
DOI : 10.1023/A:1010090405266

A. Khan, S. Mandal, R. K. Pal, and G. Saha, Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence, Scientifica, vol.16, issue.2, 2016.
DOI : 10.1371/journal.pone.0013397

A. Krogh and J. A. Hertz, A simple weight decay can improve generalization, Proceedings of the 4th International Conference on Neural Information Processing Systems. pp. 950?957. NIPS'91, 1991.

D. Martínez, G. Alenyà, T. Ribeiro, K. Inoue, C. T. Torras et al., Relational reinforcement learning for planning with exogenous effects Learning prime implicant conditions from interpretation transition, Journal of Machine Learning Research, vol.18, issue.78, pp.1-4416, 2015.

T. Ribeiro, K. Inoue, and C. Sakama, A BDD-Based Algorithm for Learning from Interpretation Transition, Proc. ILP 2013, LNAI 8812, pp.47-63, 2014.
DOI : 10.1007/978-3-662-44923-3_4
URL : https://hal.archives-ouvertes.fr/hal-01710488

T. Ribeiro, M. Magnin, K. Inoue, and C. Sakama, Learning Delayed Influences of Biological Systems, Frontiers in bioengineering and biotechnology, 2014.
DOI : 10.1145/321978.321991
URL : https://hal.archives-ouvertes.fr/hal-01710485

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

T. Ribeiro, S. Tourret, M. Folschette, M. Magnin, D. Borzacchiello et al., Inductive Learning from State Transitions over Continuous Domains, Proceedings of ILP 2017, 2017.
DOI : 10.1007/3-540-45372-5_29
URL : https://hal.archives-ouvertes.fr/hal-01655644

I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks In: Advances in neural information processing systems, pp.3104-3112, 2014.

R. J. Williams and J. Peng, An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories, Neural Computation, vol.1, issue.4, pp.490-501, 1990.
DOI : 10.1162/neco.1989.1.4.552

S. Wold, K. Esbensen, and P. Geladi, Principal component analysis Chemometrics and Intelligent Laboratory Systems, proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists, pp.37-52, 1987.