. La-liste-fournie-ci, dessus peut être obtenue de manière automatique en parcourant dynamiquement et récursivement (en suivant les clés étrangères) l'arbre de base de données, à partir d'une racine précise : la table des agents. En parcourant cet arbre, intuitivement, on s'aperçoit que le graphique de la figure 12 tend bien à montrer des Figure 39 Simulation globale : agents présents

». Spécifications, En synthèse, il s'agit d'automatiser les actions que mèneraient M

. De-rugby, Pour chacune des saisons, la génération repose sur la duplication de la structure (liste de 27 matchs) de la saison 2013/2014 de référence. On rappelle que pour chaque match d'une saison

D. Acteurvincent, note calculée (903.0) (FIN) Liste d'acteurs Cible Selected Best User -> (Thomas DOMINGO) Résultat final (<reponses><reponse><num>1</num><memoryCode>2</memoryCode><userid>8<

). R. Agrawal, S. P. Ghosh, T. Imielinski, B. R. Iyer, and A. N. Swami, An interval classifier for database mining applications, Références bibliographiques VLDB '92, pp.560-573, 1992.

). J. Dortier, la grande histoire de la psychologie, Sciences humaines, issue.7, p.44, 2008.

). A. Aamodt and E. Plaza, Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches, pp.39-59, 1994.

). R. Arkin, Y. Endo, J. B. Lee, D. C. Mackenzie, and E. Martinson, Multistrategy learning methods for multirobot systems, Proceedings of the 2nd International Workshop on Multi-Robot Systems, pp.137-150, 2003.

). R. Aumann, Subjectivity and correlation in randomized strategies, Journal of Mathematical Economics, vol.1, issue.1, pp.67-96, 1974.
DOI : 10.1016/0304-4068(74)90037-8

M. Bardou, De la stratégie à l'évaluation : des clés pour réussir un Agenda 21 local " ("From the strategy to the assessment: the keys to succeed in the elaboration of a local Agenda 21") , Collection " Références " du Service de l'Économie, de l'Évaluation, 2011.

). R. Becker, S. Zilberstein, V. Lesser, and C. V. Goldman, Transitionindependent decentralized markov decision processes, In Journal of Artificial Intelligence Research, vol.22, pp.423-455, 2004.
DOI : 10.1145/860575.860583

URL : http://anytime.cs.umass.edu/shlomo/papers/aamas03a.pdf

). F. Bellifemine, A. Poggi, and G. Rimassa, JADE ? A FIPA-compliant agent framework, 4th International Conference on Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM-99), 1999.
DOI : 10.1007/1-4020-8058-1_16

). R. Bellman, Dynamic Programming, 1957.

). D. Bernstein, R. Givan, N. Immerman, and S. Zilberstein, The Complexity of Decentralized Control of Markov Decision Processes, Mathematics of Operations Research, vol.27, issue.4, pp.819-840, 2002.
DOI : 10.1287/moor.27.4.819.297

). D. Bertsekas, Dynamic Programming: Deterministic and Stochastic Models, 1987.

). D. Bertsekas and S. Ioffe, Temporal differences-based policy iteration and applications in neuro-dynamic programming, 1996.

). 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.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

). A. Borodin and R. El-yaniv, Online Computation and Competitive Analysis, 1998.

). C. Boutilier, R. Dearden, and M. Goldszmidt, Exploiting structure in policy construction, Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp.1104-1111, 1995.

). C. Boutilier, T. Dean, and S. Hanks, Decision theoretic planning: Structural assumptions and computational leverage, Journal Of Artificial Intelligence Research, vol.11, pp.1-94, 1999.

). J. Taylor-au and K. M. Bowling, quelle(s) approche(s) de la compétence (dir) : La compétence et les nouveaux enjeux de la professionnalisationQuestions Vives" n°10 Convergence problems of general-sum multi-agent reinforcement learning Rational and convergent learning in stochastic games, proceedings of the 7th International Conference on Machine Learning) M.H. Bowling, and M. Veloso proceedings of 7th International Joint Conference on Artificial Intelligence, pp.1021-1026, 2000.

). S. Bradtke and A. G. Barto, Linear least-squares algorithms for temporal difference learning, Journal Machine Learning, vol.22, pp.1-3, 1996.

). R. Brafman, M. P. Tennenholtz-)-j, Y. Briot, . W. Demazeau-)-g, and . Brown, R-max -a general polynomial time algorithm for near-optimal reinforcement learningIntroduction aux agents: Principes et architecture des systèmes multi-agents Iterative solution of games by fictitious play, Activity Analysis of Production and Allocation, pp.374-376, 1951.

). L. Calut, S. Cammarata, D. Mcarthur, and R. Steeb, La définition et la sélection des compétences clés " , résumé du projet Definition and Selection of Competencies de l'OCDE Strategies of cooperation in distributed problem solving, Proceedings of the 8th International Joint Conference on Artificial Intelligence, 1983.

). O. Chator, J. M. Salotti, and P. A. Favier, Multi-agent System for Skills Sharing in Sustainable Development Projects, proceedings of COGNITIVE 2013, the 5th International Conference on Advanced Cognitive Technologies and Applications, IARIA Conference, pp.21-26, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00929887

). N. Chomsky, Three models for the description of language, IEEE Transactions on Information Theory, vol.2, issue.3, pp.113-124, 1956.
DOI : 10.1109/TIT.1956.1056813

). C. Claus and C. Boutilier, The dynamics of reinforcement learning in cooperative multi-agent systems, Proceedings of the 15th National Conference on Artificial Intelligence, pp.746-752, 1998.

). G. Cooper, The computational complexity of probabilistic inference using bayesian belief networks, Artificial Intelligence, vol.42, issue.2-3, pp.393-405, 1990.
DOI : 10.1016/0004-3702(90)90060-D

). D. Corkill and V. R. Lesser, The use of meta-level control for coordination in a distributed problem solving network, Proceedings of the 8th International Joint Conference on Artificial Intelligence, pp.748-756, 1983.

). A. Cornuéjols, L. Miclet, and Y. Kodratoff, Apprentissage Artificiel: Concepts et algorithmes, 2002.

). D. Coulon, J. F. Boisvieux, L. Bourrelly, L. Bruneau, E. Chouraqui et al., Le raisonnement par analogie en intelligence artificielle : formalisation, applications, Actes des 3° journées nationales PRC-GDR Intelligence artificielle, pp.45-88, 1990.

). P. Dagum and M. Luby, Approximating probabilistic inference in Bayesian belief networks is NP-hard, Artificial Intelligence, vol.60, issue.1, pp.141-153, 1993.
DOI : 10.1016/0004-3702(93)90036-B

. Darpa, R. Darpa, and . Bareiss, Case-Based Reasoning, Proceedings of a Workshop Held at the Madison Hotel, 1991.

). C. De-la-higuera, ). T. De-la-higuera, K. Dean, and . Kanazawa, Grammatical Inference: Learning Automata and Grammars A model for reasoning about persistence and causation, UK Computational Inteligence, vol.5, issue.2, pp.142-150, 1989.
DOI : 10.1017/CBO9781139194655

). S. Deen, A Computational Model for a Cooperating Agent System, Cooperative Information Agents III, proceedings of the Third International Workshop, CIA'99, pp.185-205, 1999.
DOI : 10.1007/3-540-48414-0_12

). T. Degris, P. H. Sigaud, and . Wuillemin, Learning the structure of Factored Markov Decision Processes in reinforcement learning problems, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.257-264, 2006.
DOI : 10.1145/1143844.1143877

URL : https://hal.archives-ouvertes.fr/hal-01336925

). T. Degris, P. M. Pilarski, and R. S. Sutton, Apprentissage par Renforcement sans Modèle et avec Action Continue, Actes des 7èmes Journées Francophones Planification, Décision, et Apprentissage pour la conduite de systèmes, 2012.

). M. Dickerson, R. L. Scot, J. R. Drysdale, . G. Sack-)-t, and . Diettrich, Simple algorithms for enumerating interpoint distances and finding k nearest neighbors Hierarchical reinforcement learning with the MAXQ value function decomposition, International Journal of Computational Geometry and Applications Journal of Artificial Intelligence Research, vol.2, issue.13, pp.221-239, 1992.

). P. Domingos and M. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning, pp.103-137, 1997.

). J. Dortier, Des fourmis à Internet Le mythe de l'intelligence collective, Sciences Humaines N°169, Le dossier sur « L'intelligence collective, pp.34-39, 2006.

). B. Elayeb-)-j and . Ermine, SARIPOD: Système multi-Agent de Recherche Intelligente Possibiliste de documents Web Knowledge Crash and Knowledge Management, International journal of knowledge and systems science (IJKSS), vol.1, issue.4, pp.79-95, 2009.

). J. Ferber, Multi-Agent Systems. An Introduction to Distributed Artificial Intelligence, 1999.
URL : https://hal.archives-ouvertes.fr/lirmm-00364361

). T. Finin, R. Fritzson-don, R. Mckay, and . Mcentire, KQML as an agent communication language, Proceedings of the third international conference on Information and knowledge management , CIKM '94, 1994.
DOI : 10.1145/191246.191322

). B. Fuchs, J. Lieber, A. Mille, and A. Napoli, Une première formalisation de la phase d'élaboration du raisonnement à partir de cas, Actes du 14ième atelier du raisonnement à partir de cas, 2006.

). D. Fudenberg, K. Levine, and K. , The theory of Learning in Games, 1998.

). R. Fung and K. C. Chang, Weighting and integrating evidence for stochastic simulation in Bayesian Network, Proc. of UAI, pp.209-219, 1989.

). R. Fung and B. D. Favero, Backward Simulation in Bayesian Networks, 1994.
DOI : 10.1016/B978-1-55860-332-5.50034-1

). P. Gerard, Systèmes de classeurs : étude de l'apprentissage latent, Thèse de doctorat, spécialité informatique, 2002.

). J. Gibson, The Theory of Affordances, Perceiving, Acting and Knowing, 1977.
URL : https://hal.archives-ouvertes.fr/hal-00692033

). P. Gillet, Construire la formation: outils pour les enseignants et les formateurs, PUF, 1991.

). A. Gladun and J. Rogushina, An Ontology-based approach to Student Skills in Multi-Agent E-Learning systems, International Journal " Information Technologies and Knowledge, vol.1, 2007.

M. P. Gleizes, C. Bernon, F. Migeon, G. M. Picard-)-e, and . Gold, Méthodes de développement de systèmes multi-agents " ? Ecole des Mines de Sait-Etienne ? Génie Logiciel N°86 ? Language identification in the limit, Information and Control, vol.86, issue.10 5, pp.2-7, 1967.

). J. Grabmeier and A. Rudolph, Techniques of cluster algorithms in data mining, Data Mining and Knowledge Discovery, vol.6, issue.4, pp.303-360, 2002.
DOI : 10.1023/A:1016308404627

). A. Greenwald and K. Hall, Correlated-q learning, Proceedings of the 20th International Conference on Machine Learning, pp.242-249, 2003.

M. Grundstein, Le knowledge management ou comment gérer les connaissances, Revue Problèmes Economiques, éditeur La Documentation Française, 2006.

). O. Gutknecht, J. Ferber, and E. Lieurain, Des modèles hétérogènes de simulation par systèmes multi-agents " , actes de la conférence SMAGET'98 - Modèles et Systèmes Multi-Agents pour la Gestion de l'Environnement et des Territoires, 1998.

). K. Hallouli, Reconnaissance de caractères par méthodes markoviennes et réseaux Bayésiens, Thèse de Doctorat spécialité Signal et Images, Ecole Nationale Supérieur des Télécommunications, 2004.

). K. Hammond, CHEF: a model of case-based planning, Proceedings of the 6th National Conference on Artificial Intelligence, pp.267-271, 1986.

). B. Hansen, Weather Prediction Using Case-Based Reasoning and Fuzzy Set Theory, 2000.

). T. Haynes, K. Lau, and S. Sen, Learning cases to compliment rules for conflict resolution in Multi-Agent systems, AAAI Symposium on Adaptation, Co-evolution and Learning in Multi-agents Systems, pp.51-56, 1988.

). F. Holger, O. Rogalla, and R. Dillmann, Integrating Skills into Multi-Agent Systems, In Journal of Intelligent Manufacturing, vol.9, issue.2, 1998.

). R. Howard, J. Matheson-howard, R. , and J. Matheson, Influence diagrams Readings on the principles and applications of decision analysis, Strategic Decisions Group, vol.2, pp.721-762, 1981.

). J. Hu and M. Wellman, Multi-agent reinforcement learning: theoretical framework and an algorithm, proceedings of the 15th International Conference on Machine Learning, pp.242-250, 1998.

). T. Jaakkola, M. L. Jordan, and S. P. Singh, On the Convergence of Stochastic Iterative Dynamic Programming Algorithms, Neural Computation, vol.8, issue.6, pp.1185-1201, 1994.
DOI : 10.1214/aoms/1177729586

). P. Jackson, Introduction To Expert Systems, 1998.

). J. Jacod and P. Protter, L'essentiel en théorie des probabilités, 2003.

). M. Jaczynski and B. Trousse, Fuzzy logic for the retrieval step of a case-based reasoner, pp.313-320, 1994.

). N. Jennings, M. Wooldridge, and K. Sycara, A roadmap of agent research and development, Journal of Autonomous Agents and Multi-Agent Systems, 1998.

). F. Jensen, S. Lauritzen, and . Olesen, Bayesian updating in recursive graphical models by local computations, Computational Statistical Quaterly, vol.4, pp.269-282, 1990.

). Y. Jin and T. Koyama, Multiagent planning through expectation based negotiation, 10th AAAI International Workshop on Distributed Artificial Intelligence, 1990.

). R. Kast, La théorie de la décision, 2002.

). T. Kh???lifa, J. P. Micallef, A. Quilis, J. F. Brun, A. Orsetti-)-t et al., Biom???canique de la force de pouss???e en m???l???e de rugby Self organization of a massive document collection, Sciences & Sport Neural Networks IEEE Transactions on, vol.10, issue.11, pp.163-164, 1995.

. J. Kolodner and . Kolodner, Case-Based Reasoning, 1993.

). M. Labarre, Apprentissage Multi-Agent " , Département d'informatique et de recherche opérationnelle, 2005.

). H. Labori-)-g, W. T. Lance, and . Williams, Eloge de la fuite A general theory of classificatory sorting strategies: I. Hierarchical systems, Computer Journal, vol.9, pp.373-380, 1967.

). S. Lauritzen and D. J. Spiegelhalter, Local computation with probabilities on graphical structure and their applications to expert system, Proc. of the royal statistical society, pp.154-227, 1988.

). S. Lauritzen and N. Wermuth, Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative, The Annals of Statistics, vol.17, issue.1, 1989.
DOI : 10.1214/aos/1176347003

). B. Le-blanc, J. L. Blanc, and . Ermine, A SHANNON'S THEORY OF KNOWLEDGE, Creating Collaborative Advantage through Knowledge and Innovation, pp.51-68, 2007.
DOI : 10.1142/9789812707482_0004

). Boterf, De la compétence : essai sur un attracteur étrange, Les Editions d'Organisation, 1994.

). Boterf, Ingénierie et évaluation des compétences (5ème édition), 2006.

). Boterf, Repenser la compétence. Pour dépasser les idées reçues: 15 propositions, 2008.

). M. Ledru and &. S. Michel, Capital Compétence dans l'Entreprise. Une approche cognitive, ESF éditeur, 1991.

). M. Littman, Markov games as a framework for multi-agent reinforcement learning, proceedings of the 11th International Conference on Machine Learning, pp.157-163, 1994.
DOI : 10.1016/B978-1-55860-335-6.50027-1

). M. Littman, R. S. Sutton, and S. Singh, Predictive Representation of State, Advances in Neural Information Processing Systems (NIPS-14), pp.1555-1561, 2001.

M. Littman, Friend-or-foe Q-learning in general-sum games, 8th International Conference on Machine Learning, pp.157-163, 2001.

). S. Loriette-rougegrez and . Loriette-rougegrez, Prédiction de processus à partir de comportements observés : le systèmes REBECASLoriette-Rougegrez, 1998) S. Loriette-Rougegrez Raisonnement à partir de cas pour des évolutions spatiotemporelles de processus " , revue internationale de géomatique Conduire une équipe projet, Thèse de Doctorat : InformatiqueMaders, 2000) H.P. Maders) J. Mallet, 1994.

). J. Mano, M. P. Gleizes, and P. Glize, Résolution émergente et collective de problèmes par systèmes multi-agents: principes et applications, pp.375-391, 2005.

). W. Mc-culloch, A heterarchy of values determined by the topology of nervous nets, pp.89-93, 1945.

). L. Mc-ginty and . Smyth, Collaborative Case-Based Reasoning: Applications in Personalised Route Planning, 4th International Conference on Case-Based Reasoning, ICCBR 2001, pp.362-376, 2001.
DOI : 10.1007/3-540-44593-5_26

). Sherry, Completeness criteria for retrieval in recommender systems Advances in Case-Based Reasoning, 8th European Conference (ECCBR'06), 2006.

). C. Mezrura, M. Occello, Y. Demazeau, and C. Baeijs, Récursivité dans les systèmes multi-agents : vers un modèle opérationnel, JFIADSMA'99, pp.41-52, 1999.

). R. Michalski, AQVAL/1 ? computer implementation of a variable-valued logic system VL1 and examples of its application to pattern recognition, Proceedings of First International Joint Conference on Pattern Recognition, pp.3-17, 1973.

). M. Mitchell, Machine Learning " , chap. 13 " Reinforcement Learning, pp.367-390, 1997.

). J. Mousseron, Savoir-faire, Rep. Dr. com. Dalloz, 1977.

). H. Mühlenbein-manner, R. Manderick, and B. , How Genetic Algorithms Really Work: 1. Mutation And Hill Climbing, Proceedings Of The Second Coriference On Parallel Problem Solving From Nature, pp.15-25, 1992.

). M. Mulder, T. Weigel, and K. Collins, The concept of competence in the development of vocational education and training in selected EU member states: a critical analysis, Journal of Vocational Education & Training, vol.19, issue.1, pp.67-88, 2007.
DOI : 10.3102/00346543074004557

). J. Nash, Non-Cooperative Games, The Annals of Mathematics, vol.54, issue.2, pp.286-295, 1951.
DOI : 10.2307/1969529

). J. Nash, Two-Person Cooperative Games, Econometrica, vol.21, issue.1, pp.128-140, 1953.
DOI : 10.2307/1906951

). J. Nash, The Bargaining Problem, Econometrica, vol.18, issue.2, pp.155-162, 1950.
DOI : 10.2307/1907266

). J. Nash, Equilibrium points in n-person games, Proceedings of the National Academy of Sciences, pp.48-49, 1950.

). I. Nonaka, R. Toyama, and N. Konno, SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation, Long Range Planning, vol.33, issue.1, pp.5-34, 2000.
DOI : 10.1016/S0024-6301(99)00115-6

). J. Núñez-suárez, G. Winstanley, and R. N. Griffiths, Distributed planning using a framework for DevolutionInterfaces-97: Man-machine interaction and intelligent systems in business, Proceedings of the 6th International Conference on, 1997.

). J. Odell, H. V. Parunak, and B. Bauer, Representing agent interaction protocols in UML ; Workshop on Agent-Oriented Software Engineering (AOSE'01), LNCS, pp.121-140, 2001.

). S. Ontañón, Ensemble Case Based Learning for Multi-Agents Systems, 2005.

P. J. Groupe and . Pearl, Processus décisionnels de Markov en Intelligence Artificielle Probabilistic reasoning in intelligent systems : Networks of plausible Inference, 1988.

). R. Pfeifer and J. C. Bongard, How the body shapes the way we think: a new view of intelligence, 2007.

). E. Plaza and S. Ontañon, Cooperative Multiagent Learning, Lecture Notes in Computer Science, vol.2636, pp.1-17, 2002.
DOI : 10.1007/3-540-44826-8_1

). E. Plaza and L. Mcginty, Distributed case-based reasoning, The Knowledge Engineering, Review, vol.00, pp.0-1, 2005.

). M. Prassad, V. R. Lesser, and S. E. Lander, On retrieval and reasoning in distributed case bases, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, pp.74-87, 1996.
DOI : 10.1109/ICSMC.1995.537784

). J. Prax, Le guide du knowledge management, Dunod, 2000.

). M. Putterman, Markov Decision Proceses. Discrete stochastic dynamic programming, Wiley Series in Probability and Statistics, 1994.

). J. Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1007/BF00116251

. J. Quinlan and . Quinlan, C4.5: Programs for Machine Learning, Proceedings of European Conference on Machine Learning, pp.3-20, 1993.

). J. Quinlan, Programs for Machine Learning The Netherlands, Machine Learning, pp.235-240, 1994.

). J. Robinson, An Iterative Method of Solving a Game, The Annals of Mathematics, vol.54, issue.2, pp.296-301, 1951.
DOI : 10.2307/1969530

). J. Robinson and J. , An Iterative Method of Solving a Game, The Annals of Mathematics, vol.54, issue.2, pp.296-301, 1951.
DOI : 10.2307/1969530

). G. Rocher, The Definitive Guide to Grails, Apress, 2006.
DOI : 10.1007/978-1-4302-0871-6

). C. Rodrigues, P. Gérard, and C. , Apprentissage incrémental de règles d'actions relationnelles, Actes du 17ème congrés pour la Reconnaissance des Formes et Intelligence Artificielle, 2010.

). J. Routier, P. Mathieu, and Y. Secq, Dynamic Skills Learning : a Support to Agent Evolution, 5th Pacific Rim International Workshop on Multi-Agents, PRIMA 2002 Tokyo, Proceedings, pp.109-122, 2002.

). D. Roux, La notion de compétence " , http://formation.acbordeaux .fr/pedagogie/ress_pedago/prod_inspection/socle/notion_competence, Rummery, 1994) G.A. Rummery, and M. Nirajan, "Online Q-Learning using Connectionist Systems, 1994.

). S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, 2003.

). D. Rychen and L. H. Salganik, Defining and Selecting Key Competencies, 2001.

). S. Salotti, Filtrage flou et représentation centrée objet pour raisonner par analogie : le système FLORAN, Thèse de Doctorat : Informatique, 1992.

. Ch, V. Salperwyck, and . Lemaire, Classification incrémentale supervisée: un panel introductif, Actes de EGC, 2009.

). A. Dawid, Applications of a general propagation algorithm for probabilistic expert systems, Statistics and Computing, vol.2, issue.1, pp.25-36, 1992.
DOI : 10.1007/BF01890546

). R. Schank, Dynamic Memory: A Theory of Learning in Computers and People, 1982.

). R. Schank, EXPLANATIONS, MACHINE LEARNING, AND CREATIVITY, Machine Learning: An Artificial Intelligence Approach IV, pp.31-48, 1990.
DOI : 10.1016/B978-0-08-051055-2.50005-5

). J. Schmidhuber, J. Zhao, and M. Wiering, Simple Principle of Metalearning, pp.69-96, 1996.

). R. Shachter and M. A. Peot, Simulation Approaches to General Probabilistic Inference on Belief Networks, Proc. of UAI, pp.311-318, 1989.
DOI : 10.1016/B978-0-444-88738-2.50024-5

). R. Shank, Dynamic Memory, 1982.

). L. Shapiro, Embodied Cognition " , in Oxford Handbook of Philosophy and Cognitive Science, 2010.

). L. Shapley, Stochastic Games, Proceedings of the National Academy of Sciences, vol.39, issue.10, pp.1095-1100, 1953.
DOI : 10.1073/pnas.39.10.1953

). S. Shiu, K. Shiu, D. S. Yeung, C. H. Sun, and X. Z. Wang, Transferring Case Knowledge To Adaptation Knowledge: An Approach for Case-Base Maintenance, Computational Intelligence, vol.17, issue.2, pp.295-314, 2001.
DOI : 10.1111/0824-7935.00146

). H. Simon, The Sciences of the Artificial, 1969.

). S. Singh, M. Kearns, and Y. Mansour, Nash convergence of gradient dynamics in general sum games, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp.541-548, 2000.

). B. Stinson, PostgreSQL Essential reference, 2002.

). R. 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

). R. Sutton, C. Szepesvari, A. Geramifard, and M. H. Bowling, Dyna-style planning with linear function approximation and prioritized sweeping, Proceedings of 24th Conference of Uncertainty in artificial intelligence, pp.528-536, 2008.

). P. Tolchinsky, S. Modgil, U. Cortes, and M. Sanchez-marre, Cbr and argument schemes for collaborative decision making, Proceedings of the 6th conference on Computational Models of Argument, pp.71-82, 2006.

). V. Vapnik, Statistical learning theory, 1998.

). F. Varela, E. Thompson, and E. Rosch, The Embodied Mind: Cognitive Science and Human Experience, 1991.

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

). G. Weiss-)-p and . Winston, Multiagent Systems, a Modern Approach to Distributed Artificial Intelligence Learning Structural Descriptions from Examples The Psychology of Computer Vision, pp.157-209, 1975.

). M. Wooldridge and N. Jenning, Pitfalls of agent-oriented development, Proceedings of the second international conference on Autonomous agents , AGENTS '98, 1998.
DOI : 10.1145/280765.280867

). H. Young, Introduction à la théorie des jeux Individual Strategy and Social Structure: An Evolutionary Theory of Instituions, 1998.

R. Yua, B. Iunga, and H. Panetto, A multi-agents based E-maintenance system with case-based reasoning decision support, Engineering Applications of Artificial Intelligence, vol.16, issue.4, pp.321-333, 2003.
DOI : 10.1016/S0952-1976(03)00079-4

). M. Zaki and C. J. Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, Proceedings of the Second SIAM International Conference on Data Mining, pp.12-28, 2002.
DOI : 10.1137/1.9781611972726.27

). P. Zarifian, La politique de la compétence et l'appel aux connaissances à partir de la stratégie d'entreprise post-fordiste, 1er Colloque du groupe de travail Gestion des Compétences et des Connaissances en Génie Industriel, 2002.

. Utilisateurs-humains-du-système, Un « modèle comportemental » dédié et évolutif est proposé dans le but d