A. Categorization, 59 3.1.2.1 Reflexive Actions, External Actions, p.60

.. Attention-selection-mechanism, 63 3.2.1 High-Level Attention, p.64

P. Expectation and M. , 87 3.4.4.1 Finding a Sequence of Observations, .4.4.2 Parameters Initialization and Stopping Criterion 88 3.4.4.3 Merging the Results of Expectation- Maximization algorithms (EMs) . . . . . . . . 88

S. Refinement and .. , 94 4.1.1 Choice of the Stimuli, Low Level Stimuli . . . . . . . . . . . . . . . . 95

.. Attention-selection-mechanism, 101 4.2.1 Choice of the Values of Attention Functions 101 4.2.2 Consequences of Attention Selection, Decrease in the Number of Parameters . . . . 102 4.2.2.2 Increase in Expressiveness . . . . . . . . . . . . 104

.. Learning-the-environment, 106 4.3.1 Measures and Representation of the Results

E. Characteristics-of-the, 125 4.4.2.1 Evolution of the Likelihoods, 125 4.4.2.2 Effect of the Merging of the Parameters . . . . 128 4.4.2.3 Sequence of Decisions . . . . . . . . . . . . . . 130

R. Behaviours and .. , 131 4.4.3.1 Study of the Distributions, Believability, p.139

F. Work and .. , 151 5.4.1 Extension of the Attention Selection Mechanism, p.153

J. Anderson, Rules of the mind, p.22, 1993.

D. Angluin, Inference of Reversible Languages, Journal of the ACM, vol.29, issue.3, pp.741-765, 1982.
DOI : 10.1145/322326.322334

D. Angluin, Computational learning theory, Proceedings of the twenty-fourth annual ACM symposium on Theory of computing , STOC '92, pp.351-369, 1992.
DOI : 10.1145/129712.129746

T. Artieres, S. Marukatat, and P. Gallinari, Online Handwritten Shape Recognition Using Segmental Hidden Markov Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.2, pp.205-217, 2007.
DOI : 10.1109/TPAMI.2007.38
URL : https://hal.archives-ouvertes.fr/hal-01170742

J. Bates, The nature of characters in interactive worlds and the Oz project, p.22, 1992.

C. Bauckhage, B. Gorman, C. Thurau, and M. Humphrys, Learning Human Behavior from Analyzing Activities in Virtual Environments, pp.3-17, 2007.

C. Bauckhage and C. Thurau, Towards a Fair'n Square Aimbot?Using Mixtures of Experts to Learn Context Aware Weapon Handling, Proc. GAME-ON, pp.20-24, 2004.

L. Baum, T. Petrie, G. Soules, and N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The annals of mathematical statistics, pp.164-171, 1970.

Y. Bengio and P. Frasconi, An input output HMM architecture, Advances in neural information processing systems, pp.427-434, 1995.

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.3-4561, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

S. Blackmore, The meme machine, p.30, 1999.

R. Bozinovic and S. Srihari, A string correction algorithm for cursive script recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.6, pp.655-663, 1982.

B. Bryant and R. Miikkulainen, Evolving Stochastic Controller Networks for Intelligent Game Agents, 2006 IEEE International Conference on Evolutionary Computation, pp.3752-3759, 2006.
DOI : 10.1109/CEC.2006.1688419

G. C. Burdea and P. Coiffet, Virtual Reality Technology, Presence: Teleoperators and Virtual Environments, vol.12, issue.6, 2003.
DOI : 10.1162/105474603322955950

O. Burkert, R. Kadlec, J. Gemrot, M. Bida, J. Havlicek et al., Towards Fast Prototyping of IVAs Behavior: Pogamut 2, Intelligent Virtual Agents, pp.362-363, 2007.
DOI : 10.1007/978-3-540-74997-4_39

S. Calinon and A. Billard, Incremental learning of gestures by imitation in a humanoid robot, Proceeding of the ACM/IEEE international conference on Human-robot interaction , HRI '07, pp.255-262, 2007.
DOI : 10.1145/1228716.1228751

S. Cass, Mind games [computer game AI], IEEE Spectrum, vol.39, issue.12, pp.40-44, 2002.
DOI : 10.1109/MSPEC.2002.1088444

M. Cavazza, F. Charles, M. , and S. , Character-based interactive storytelling, IEEE Intelligent Systems, vol.17, issue.4, pp.17-24, 2002.
DOI : 10.1109/MIS.2002.1024747

D. Charles and S. Mcglinchey, The past, present and future of artificial neural networks in digital games, Proceedings of the 5th international conference on computer games: artificial intelligence, pp.163-169, 2004.

C. Crawford, Chris Crawford on Interactive Storytelling. New Riders Games, 2004.

M. Crouse, R. Nowak, and R. Baraniuk, Wavelet-based statistical signal processing using hidden Markov models, IEEE Transactions on Signal Processing, vol.46, issue.4, pp.886-902, 1998.
DOI : 10.1109/78.668544
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.320.2336

A. Dempster, N. Laird, R. , and D. , Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, issue.1, pp.1-38, 1977.

S. Donikian, HPTS, Proceedings of the fifth international conference on Autonomous agents , AGENTS '01, pp.401-408, 2001.
DOI : 10.1145/375735.376382

R. Dromey, From requirements to design: formalizing the key steps, First International Conference onSoftware Engineering and Formal Methods, 2003.Proceedings., pp.2-11, 2003.
DOI : 10.1109/SEFM.2003.1236202

J. Feldman and D. Ballard, Connectionist Models and Their Properties, Cognitive Science, vol.284, issue.1, pp.205-254, 1982.
DOI : 10.1207/s15516709cog0603_1

R. Fikes and N. Nilsson, Strips: A new approach to the application of theorem proving to problem solving, Artificial Intelligence, vol.2, issue.3-4, pp.189-208, 1971.
DOI : 10.1016/0004-3702(71)90010-5

G. Florez-larrahondo, Incremental learning of discrete hidden Markov models, pp.43-74, 2005.

B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems 7, pp.625-632, 1995.

J. Gibson, The ecological approach to visual perception, 1986.

J. Glass, J. Chang, and M. Mccandless, A probabilistic framework for feature-based speech recognition, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, pp.2277-2280, 1996.
DOI : 10.1109/ICSLP.1996.607261

B. Gorman and M. Humphrys, Imitative learning of combat behaviours in first-person computer games, Proceedings of CGAMES 2007, the 11th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games, pp.18-33, 2007.

B. Gorman, C. Thurau, C. Bauckhage, and M. Humphrys, Bayesian Imitation of Human Behavior in Interactive Computer Games, 18th International Conference on Pattern Recognition (ICPR'06), pp.1244-1247, 2006.
DOI : 10.1109/ICPR.2006.317

B. Gorman, C. Thurau, C. Bauckhage, and M. Humphrys, Believability Testing and Bayesian Imitation in Interactive Computer Games, From Animals to Animats 9, pp.655-666, 2006.
DOI : 10.1007/11840541_54

C. Heeter, There: The Subjective Experience of Presence, Presence: Teleoperators and Virtual Environments, vol.1, issue.2, pp.262-271, 1992.
DOI : 10.1162/pres.1992.1.2.262

R. Held and N. Durlach, Telepresence, time delay and adaptation. Pictorial communication in virtual and real environments, pp.232-246, 1991.

G. Hinton, Connectionist learning procedures, Artificial Intelligence, vol.40, issue.1-3, pp.185-234, 1989.
DOI : 10.1016/0004-3702(89)90049-0

H. G. Hoffman, T. Richards, D. , P. , D. et al., The Illusion of Presence in Immersive Virtual Reality during an fMRI Brain Scan, CyberPsychology & Behavior, vol.6, issue.2, pp.127-131, 2003.
DOI : 10.1089/109493103321640310

D. Isla, Handling complexity in the halo 2 ai, Game Developers Conference, p.17, 2005.

D. Johnson and J. Wiles, Computer games with intelligence, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), pp.1355-1358, 2001.
DOI : 10.1109/FUZZ.2001.1008909

A. Krogh, B. Larsson, G. Von-heijne, and E. Sonnhammer, Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F. Cohen, Journal of Molecular Biology, vol.305, issue.3, pp.567-580, 2001.
DOI : 10.1006/jmbi.2000.4315

J. Laird and J. Duchi, Creating human-like synthetic characters with multiple skill levels: a case study using the Soar Quakebot, Simulating Human Agents, Papers from the 2000 AAAI Fall Symposium, pp.75-79, 2000.

J. Laird and M. Lent, Human-level ai's killer application: Interactive computer games, pp.15-26, 2001.

J. Laird, P. Rosenbloom, and A. Newell, Chunking in Soar: The anatomy of a general learning mechanism, Machine Learning, vol.7, issue.1, pp.11-46, 1986.
DOI : 10.1007/BF00116249

L. Hy and R. , Programmation et apprentissage bayésien de comportements pour des personnages synthétiques, application aux personnages de jeux vidéos, pp.39-41, 2007.

L. Hy, R. Arrigoni, A. Bessì-ere, P. Lebeltel, and O. , Teaching bayesian behaviours to video game characters, Robotics and Autonomous Systems, vol.47, issue.57, pp.177-185, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00182073

S. Levinson, An introduction to the application of the theory of probabilistic functions of a markov process to automatic speech recognition. The Bell System Technical Journal, p.24, 1983.

D. Livingstone, Turing's test and believable AI in games, Computers in Entertainment, vol.4, issue.1, 2006.
DOI : 10.1145/1111293.1111303

J. Loomis, Distal Attribution and Presence, Presence: Teleoperators and Virtual Environments, vol.2, issue.1, pp.113-119, 1992.
DOI : 10.1121/1.400250

A. B. Loyall, Believable agents: building interactive personalities, p.22, 1997.

M. Namee and B. , Proactive persistent agents: using situational intelligence to create support characters in character-centric computer games, p.10, 2004.

J. Mari, D. Fohr, and J. Junqua, A second-order HMM for high performance word and phoneme-based continuous speech recognition, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, pp.435-438, 1996.
DOI : 10.1109/ICASSP.1996.541126

A. Mcmahan, Immersion, engagement and presence. The video game theory reader, pp.67-86, 2003.

A. Meltzoff and M. Moore, Imitation of Facial and Manual Gestures by Human Neonates, Science, vol.198, issue.4312, pp.75-78, 1977.
DOI : 10.1126/science.198.4312.75

T. Mitchell, Machine Learning, p.27, 1997.

K. Murphy, Dynamic Bayesian networks: representation, inference and learning, p.37, 2002.

A. Newell, P. S. Rosenbloom, L. , and J. E. , Soar: an architecture for general intelligence, Artificial intelligence, vol.33, issue.1, pp.1-64, 1987.

J. Orkin, Three States and a Plan: The AI of FEAR, Proceedings of the 2006 Game Developers Conference. xix, p.23, 2006.

D. Pinchbeck, Trigens can't swim: intelligence and intentionality in first person game worlds, Proceedings of The Philosophy of Computer Games, 2008.

L. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

R. Rao, A. Shon, and A. Meltzoff, A Bayesian model of imitation in infants and robots, Imitation and Social Learning in Robots, Humans, and Animals, pp.217-248, 2004.
DOI : 10.1017/CBO9780511489808.016

W. Reilly, Believable social and emotional agents, p.22, 1996.

M. Riedl and A. Stern, Believable agents and intelligent story adaptation for interactive storytelling. Technologies for Interactive Digital Storytelling and Entertainment, pp.1-12, 2006.

M. Riedl and R. Young, An Objective Character Believability Evaluation Procedure for Multi-agent Story Generation Systems, Intelligent Virtual Agents, pp.278-291, 2005.
DOI : 10.1007/11550617_24
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.8065

G. Robert and A. Guillot, A motivational architecture of action selection for non-player characters in dynamic environments, International Journal of Intelligent Games & Simulation, vol.4, issue.1, pp.1-12, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01185697

S. Sanchez, H. Luga, and Y. Duthen, Learning Classifier Systems and Behavioural Animation of Virtual Characters, Intelligent Virtual Agents, pp.467-467, 2006.
DOI : 10.1007/11821830_54

S. Schaal, Is imitation learning the route to humanoid robots?, Trends in Cognitive Sciences, vol.3, issue.6, pp.233-242, 1999.
DOI : 10.1016/S1364-6613(99)01327-3

R. Shepard, Toward a universal law of generalization for psychological science, Science, vol.237, issue.4820, pp.1317-58, 1987.
DOI : 10.1126/science.3629243

O. Sigaud and S. Wilson, Learning classifier systems: a survey. Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol.11, issue.11, pp.1065-1078, 2007.

R. Simmons and S. Koenig, Probabilistic robot navigation in partially observable environments, International Joint Conference on Artificial Intelligence, pp.1080-1087, 1995.

M. Slater, V. Linakis, M. Usoh, R. Kooper, and G. Street, Immersion , presence, and performance in virtual environments: An experiment with tri-dimensional chess, ACM Virtual Reality Software and Technology (VRST, pp.163-172, 1996.

M. Slater, M. Usoh, and A. Steed, Taking steps: the influence of a walking technique on presence in virtual reality, ACM Transactions on Computer-Human Interaction, vol.2, issue.3, pp.201-219, 1995.
DOI : 10.1145/210079.210084

K. Stanley, B. Bryant, and R. Miikkulainen, Evolving neural network agents in the nero video game, Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, pp.182-189, 2005.

J. Steuer, Defining Virtual Reality: Dimensions Determining Telepresence, Journal of Communication, vol.1, issue.1, pp.73-93, 1992.
DOI : 10.1111/j.1460-2466.1992.tb00812.x
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.5821

F. Tencé and C. Buche, Automatable evaluation method oriented toward behaviour believability for video games, GAME-ON 2008 9th International Conference on Intelligent Games and Simulation, pp.39-43, 2008.

F. Tencé, C. Buche, P. De-loor, M. , and O. , The challenge of believability in video games: Definitions, agents models and imitation learning, GAMEON-ASIA'2010, 2nd Asian Conference on Simulation and AI in Computer Games, pp.38-45, 2010.

F. Thomas and O. Johnston, Disney animation: the illusion of life, 1981.

S. Thrun, Learning to play the game of chess, Advances in Neural Information Processing Systems, pp.1069-1076, 1995.

C. Thurau, C. Bauckhage, and G. Sagerer, Learning human-like movement behavior for computer games, Proceedings of the 8th International Conference on the Simulation of Adaptive Behavior, pp.31-33, 2004.

C. Thurau, T. Paczian, and C. Bauckhage, Is bayesian imitation learning the route to believable gamebots, GAMEON-NA'2005, pp.3-9, 2005.

A. Vinciarelli, A survey on off-line cursive script recognition. Patter Recognition, p.24, 2002.
DOI : 10.1016/s0031-3203(01)00129-7
URL : http://infoscience.epfl.ch/record/82634

J. Weizenbaum, ELIZA---a computer program for the study of natural language communication between man and machine, Communications of the ACM, vol.9, issue.1, pp.36-45, 1966.
DOI : 10.1145/365153.365168

B. Wetzel, Step one: Document the problem, Challenges in Game Artificial Intelligence: Papers from the 2004 AAAI Workshop, pp.11-15, 2004.

C. Wu, On the convergence properties of the em algorithm. The Annals of Statistics, pp.95-103, 1983.

J. Yamato, J. Ohya, and K. Ishii, Recognizing human action in timesequential images using hidden markov model, Computer Vision and Pattern Recognition Proceedings CVPR'92. IEEE Computer Society Conference on, pp.379-385, 1992.

Z. Zeng, R. Goodman, and P. Smyth, Learning Finite State Machines With Self-Clustering Recurrent Networks, Neural Computation, vol.5, issue.6, pp.976-990, 1993.
DOI : 10.1162/neco.1989.1.2.270
URL : http://authors.library.caltech.edu/13654/1/ZENnc93.pdf