L. B. Meyer, Emotion and Meaning in Music, 1956.
DOI : 10.7208/chicago/9780226521374.001.0001

R. Rosen, Anticipatory Systems, volume 1 of IFSR International Series on Systems Science and Engineering, 1985.

A. Lejaren, L. M. Hiller, and . Isaacson, Experimental Music: Composition with an Electronic Computer, 1959.

I. Xenakis, Formalized Music, 1971.

D. Conklin, Music generation from statistical models, Proceedings of the AISB 2003 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences

J. Ziv and A. Lempel, Compression of individual sequences via variable-rate coding, IEEE Transactions on Information Theory, vol.24, issue.5, pp.530-536, 1978.
DOI : 10.1109/TIT.1978.1055934

M. Feder, N. Merhav, and M. Gutman, Universal prediction of individual sequences, IEEE Transactions on Information Theory, vol.38, issue.4, pp.1258-1270, 1992.
DOI : 10.1109/18.144706

S. Dubnov, R. El-yaniv, and G. Assayag, Universal classification applied to musical sequences, Proceedings of International Computer Music Conference, pp.322-340, 1998.
URL : https://hal.archives-ouvertes.fr/hal-01161365

S. Dubnov, G. Assayag, O. Lartillot, and G. Bejerano, Using machine-learning methods for musical style modeling, Computer, vol.36, issue.10, pp.73-80, 2003.
DOI : 10.1109/MC.2003.1236474

F. Pachet, The Continuator: Musical Interaction With Style, Proc. of International Computer Music Conference, 2002.
DOI : 10.1076/jnmr.32.3.333.16861

D. Ron, Y. Singer, and N. Tishby, The power of amnesia: Learning probabilistic automata with variable memory length, Machine Learning, pp.117-149, 1996.
DOI : 10.1007/BF00114008

G. Assayag and S. Dubnov, Using Factor Oracles for Machine Improvisation, Soft Computing, vol.8, issue.9, pp.604-610, 2004.
DOI : 10.1007/s00500-004-0385-4
URL : https://hal.archives-ouvertes.fr/hal-01161221

C. Allauzen, M. Crochemore, and M. Raffinot, Factor Oracle: A New Structure for Pattern Matching, Proc. of Conference on Current Trends in Theory and Practice of Informatics, pp.295-310, 1999.
DOI : 10.1007/3-540-47849-3_18
URL : https://hal.archives-ouvertes.fr/hal-00619846

J. A. Biles, Genjam in perspective: A tentative taxonomy for genetic algorithm music and art systems, Genetic Algorithms in Visual Art and Music, pp.133-135, 2000.

J. A. Franklin, Predicting reinforcement of pitch sequences via lstm and td, Proc. of International Computer Music Conference, 2004.

D. Conklin and I. Witten, Multiple viewpoint systems for music prediction, Journal of New Music Research, pp.51-73, 1995.
DOI : 10.2307/3680523

D. Huron, Sweet Anticipation: Music and the Psychology of Expectation, 2006.

G. Edelman, Neural Darwinism: The Theory of Neuronal Group Selection, Basic Books, 1987.

B. Snyder, Music and Memory: An Introduction.M I TP r e s s ,N e wY o r k

M. Butz, O. Sigaud, and P. Gérard, Internal Models and Anticipations in Adaptive Learning Systems, Anticipatory Behavior in Adaptive Learning Systems
DOI : 10.1007/978-3-540-45002-3_6

P. Davidsson, A Framework for Preventive State Anticipation, Anticipatory Behavior in Adaptive Learning Systems, pp.151-166, 2003.
DOI : 10.1007/978-3-540-45002-3_9

S. Dubnov, Spectral Anticipations, Computer Music Journal, vol.13, issue.2, 2006.
DOI : 10.1038/nn831

A. Lefebvre and T. Lecroq, Computing repeated factors with a factor oracle, Proceedings of the11th Australasian Workshop On Combinatorial Algorithms, pp.145-158, 2000.

R. S. Sutton, Dyna, an integrated architecture for learning, planning, and reacting, Working Notes of the AAAI Spring Symposium on Integrated Intelligent Architectures
DOI : 10.1145/122344.122377

E. Uchibe and K. Doya, Competitive-cooperative-concurrent reinforcement learning with importance sampling, Proc. of International Conference on Simulation of Adaptive Behavior: From Animals and Animats, pp.287-296, 2004.

A. Moore and C. Atkeson, Prioritized sweeping: Reinforcement learning with less data and less time, Machine Learning, pp.103-130, 1993.
DOI : 10.1007/BF00993104