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chapters/TS.aux View file @ 2a235a1
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main.aux View file @ 2a235a1
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  94 +\bibcite{NGUYEN2024111566}{Nguyen, 2024}
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main.log View file @ 2a235a1
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  1 +\babel@toc {french}{}\relax
  2 +\contentsline {part}{I\hspace {1em}Contexte et Problématiques}{1}{part.1}%
  3 +\contentsline {chapter}{\numberline {1}Introduction}{3}{chapter.1}%
  4 +\contentsline {section}{\numberline {1.1}Contributions Principales}{4}{section.1.1}%
  5 +\contentsline {section}{\numberline {1.2}Plan de la thèse}{5}{section.1.2}%
  6 +\contentsline {part}{II\hspace {1em}État de l'art}{7}{part.2}%
  7 +\contentsline {chapter}{\numberline {2}Contexte}{9}{chapter.2}%
  8 +\contentsline {section}{\numberline {2.1}Les stratégies d'apprentissage humain et les environnements informatiques pour l'apprentissage humain (EIAH)}{9}{section.2.1}%
  9 +\contentsline {subsection}{\numberline {2.1.1}Les stratégies d'apprentissage}{9}{subsection.2.1.1}%
  10 +\contentsline {subsection}{\numberline {2.1.2}Les EIAH}{10}{subsection.2.1.2}%
  11 +\contentsline {subsection}{\numberline {2.1.3}L'exerciseur initial AI-VT}{11}{subsection.2.1.3}%
  12 +\contentsline {section}{\numberline {2.2}Le contexte technique}{12}{section.2.2}%
  13 +\contentsline {subsection}{\numberline {2.2.1}Le raisonnement à partir de cas (RàPC)}{12}{subsection.2.2.1}%
  14 +\contentsline {subsubsection}{\numberline {2.2.1.1}Retrouver (Rechercher)}{14}{subsubsection.2.2.1.1}%
  15 +\contentsline {subsubsection}{\numberline {2.2.1.2}Réutiliser (Adapter)}{14}{subsubsection.2.2.1.2}%
  16 +\contentsline {subsubsection}{\numberline {2.2.1.3}Réviser et Réparer}{14}{subsubsection.2.2.1.3}%
  17 +\contentsline {subsubsection}{\numberline {2.2.1.4}Retenir (Stocker)}{15}{subsubsection.2.2.1.4}%
  18 +\contentsline {subsubsection}{\numberline {2.2.1.5}Conteneurs de Connaissance}{16}{subsubsection.2.2.1.5}%
  19 +\contentsline {subsection}{\numberline {2.2.2}Les systèmes multi-agents}{17}{subsection.2.2.2}%
  20 +\contentsline {subsection}{\numberline {2.2.3}Différents algorithmes et fonctions implémentés dans AI-VT pour la personnalisation et l'adaptation des séances d'entraînement proposées}{17}{subsection.2.2.3}%
  21 +\contentsline {subsubsection}{\numberline {2.2.3.1}Pensée Bayesienne}{17}{subsubsection.2.2.3.1}%
  22 +\contentsline {subsubsection}{\numberline {2.2.3.2}Méthode des k plus proches voisins (K-Nearest Neighborhood - KNN)}{18}{subsubsection.2.2.3.2}%
  23 +\contentsline {subsubsection}{\numberline {2.2.3.3}K-Moyennes}{19}{subsubsection.2.2.3.3}%
  24 +\contentsline {subsubsection}{\numberline {2.2.3.4}Modèle de Mélange Gaussien GMM (\textit {Gaussian Mixture Model})}{20}{subsubsection.2.2.3.4}%
  25 +\contentsline {subsubsection}{\numberline {2.2.3.5}Fuzzy-C}{20}{subsubsection.2.2.3.5}%
  26 +\contentsline {subsubsection}{\numberline {2.2.3.6}Bandit Manchot MAB (\textit {Multi-Armed Bandits})}{21}{subsubsection.2.2.3.6}%
  27 +\contentsline {subsubsection}{\numberline {2.2.3.7}Échantillonnage de Thompson TS (\textit {Thompson Sampling})}{21}{subsubsection.2.2.3.7}%
  28 +\contentsline {chapter}{\numberline {3}Environnements Informatiques d'Apprentissage Humain}{23}{chapter.3}%
  29 +\contentsline {section}{\numberline {3.1}L'Intelligence Artificielle}{23}{section.3.1}%
  30 +\contentsline {section}{\numberline {3.2}Systèmes de recommandation dans les EIAH}{24}{section.3.2}%
  31 +\contentsline {chapter}{\numberline {4}\'Etat de l'art (Raisonnement à Partir de Cas)}{29}{chapter.4}%
  32 +\contentsline {section}{\numberline {4.1}Intégration d'un réseau de neurones dans le cycle du RàPC}{29}{section.4.1}%
  33 +\contentsline {section}{\numberline {4.2}Le RàPC pour construire ou corriger un texte, pour consolider ou expliquer les résultats obtenus}{30}{section.4.2}%
  34 +\contentsline {section}{\numberline {4.3}Travaux récents sur la représentation des cas et le cycle du RàPC}{31}{section.4.3}%
  35 +\contentsline {section}{\numberline {4.4}Intégration d'autres algorithmes dans le cycle du RàPC}{33}{section.4.4}%
  36 +\contentsline {section}{\numberline {4.5}Prédiction et interpolation en utilisant le RàPC}{34}{section.4.5}%
  37 +\contentsline {section}{\numberline {4.6}Recommandation, EIAH et RàPC}{35}{section.4.6}%
  38 +\contentsline {section}{\numberline {4.7}Récapitulatif des limites des travaux présentés dans ce chapitre}{36}{section.4.7}%
  39 +\contentsline {part}{III\hspace {1em}Contributions}{39}{part.3}%
  40 +\contentsline {chapter}{\numberline {5}Architecture Globale du Système AI-VT}{41}{chapter.5}%
  41 +\contentsline {section}{\numberline {5.1}Introduction}{41}{section.5.1}%
  42 +\contentsline {section}{\numberline {5.2}Description du système AI-VT}{42}{section.5.2}%
  43 +\contentsline {section}{\numberline {5.3}Modèle d'architecture proposé}{44}{section.5.3}%
  44 +\contentsline {subsection}{\numberline {5.3.1}Correction automatique}{46}{subsection.5.3.1}%
  45 +\contentsline {subsection}{\numberline {5.3.2}Identification}{47}{subsection.5.3.2}%
  46 +\contentsline {subsection}{\numberline {5.3.3}Révision}{47}{subsection.5.3.3}%
  47 +\contentsline {subsection}{\numberline {5.3.4}Test}{48}{subsection.5.3.4}%
  48 +\contentsline {section}{\numberline {5.4}Conclusion}{49}{section.5.4}%
  49 +\contentsline {chapter}{\numberline {6}Le raisonnement à partir de cas (RàPC) pour la régression}{51}{chapter.6}%
  50 +\contentsline {section}{\numberline {6.1}Introduction}{51}{section.6.1}%
  51 +\contentsline {section}{\numberline {6.2}Apprentissage par empilement et raisonnement à partir de cas}{53}{section.6.2}%
  52 +\contentsline {subsection}{\numberline {6.2.1}Algorithme Proposé}{53}{subsection.6.2.1}%
  53 +\contentsline {subsubsection}{\numberline {6.2.1.1}Rechercher}{54}{subsubsection.6.2.1.1}%
  54 +\contentsline {subsubsection}{\numberline {6.2.1.2}Réutiliser}{55}{subsubsection.6.2.1.2}%
  55 +\contentsline {subsubsection}{\numberline {6.2.1.3}Révision}{59}{subsubsection.6.2.1.3}%
  56 +\contentsline {subsubsection}{\numberline {6.2.1.4}Mémorisation}{60}{subsubsection.6.2.1.4}%
  57 +\contentsline {subsection}{\numberline {6.2.2}Résultats}{61}{subsection.6.2.2}%
  58 +\contentsline {subsection}{\numberline {6.2.3}Discussion}{62}{subsection.6.2.3}%
  59 +\contentsline {subsection}{\numberline {6.2.4}Conclusion}{63}{subsection.6.2.4}%
  60 +\contentsline {section}{\numberline {6.3}ESCBR-SMA : Introduction des systèmes multi-agents dans ESCBR}{63}{section.6.3}%
  61 +\contentsline {subsection}{\numberline {6.3.1}Algorithme Proposé}{64}{subsection.6.3.1}%
  62 +\contentsline {subsubsection}{\numberline {6.3.1.1}Algorithmes}{65}{subsubsection.6.3.1.1}%
  63 +\contentsline {subsubsection}{\numberline {6.3.1.2}Structure des agents}{67}{subsubsection.6.3.1.2}%
  64 +\contentsline {subsubsection}{\numberline {6.3.1.3}Apprentissage des agents}{67}{subsubsection.6.3.1.3}%
  65 +\contentsline {subsubsection}{\numberline {6.3.1.4}Échanges entre les agents}{69}{subsubsection.6.3.1.4}%
  66 +\contentsline {subsection}{\numberline {6.3.2}Résultats}{69}{subsection.6.3.2}%
  67 +\contentsline {subsection}{\numberline {6.3.3}Conclusion}{72}{subsection.6.3.3}%
  68 +\contentsline {chapter}{\numberline {7}Système de Recommandation dans AI-VT}{73}{chapter.7}%
  69 +\contentsline {section}{\numberline {7.1}Introduction}{73}{section.7.1}%
  70 +\contentsline {section}{\numberline {7.2}Système de recommandation stochastique fondé sur l'échantillonnage de Thompson}{74}{section.7.2}%
  71 +\contentsline {subsection}{\numberline {7.2.1}Algorithme Proposé}{74}{subsection.7.2.1}%
  72 +\contentsline {subsection}{\numberline {7.2.2}Résultats}{76}{subsection.7.2.2}%
  73 +\contentsline {subsection}{\numberline {7.2.3}Discussion et Conclusion}{81}{subsection.7.2.3}%
  74 +\contentsline {section}{\numberline {7.3}ESCBR-SMA et échantillonnage de Thompson}{83}{section.7.3}%
  75 +\contentsline {subsection}{\numberline {7.3.1}Concepts Associés}{84}{subsection.7.3.1}%
  76 +\contentsline {subsection}{\numberline {7.3.2}Algorithme Proposé}{86}{subsection.7.3.2}%
  77 +\contentsline {subsection}{\numberline {7.3.3}Résultats et Discussion}{88}{subsection.7.3.3}%
  78 +\contentsline {subsubsection}{\numberline {7.3.3.1}Régression avec ESCBR-SMA pour l'aide à l'apprentissage humain}{88}{subsubsection.7.3.3.1}%
  79 +\contentsline {subsubsection}{\numberline {7.3.3.2}Progression des connaissances}{89}{subsubsection.7.3.3.2}%
  80 +\contentsline {subsubsection}{\numberline {7.3.3.3}Système de recommandation avec un jeu de données d'étudiants réels}{91}{subsubsection.7.3.3.3}%
  81 +\contentsline {subsubsection}{\numberline {7.3.3.4}Comparaison entre TS et BKT}{92}{subsubsection.7.3.3.4}%
  82 +\contentsline {subsubsection}{\numberline {7.3.3.5}Système de recommandation avec ESCBR-SMA}{93}{subsubsection.7.3.3.5}%
  83 +\contentsline {subsubsection}{\numberline {7.3.3.6}Progression des connaissances TS vs TS et ESCBR-SMA}{93}{subsubsection.7.3.3.6}%
  84 +\contentsline {subsection}{\numberline {7.3.4}Conclusion}{95}{subsection.7.3.4}%
  85 +\contentsline {section}{\numberline {7.4}ESCBR-SMA, échantillonnage de Thompson et processus de Hawkes}{96}{section.7.4}%
  86 +\contentsline {subsection}{\numberline {7.4.1}Algorithme Proposé}{96}{subsection.7.4.1}%
  87 +\contentsline {subsection}{\numberline {7.4.2}Résultats et Discussion}{98}{subsection.7.4.2}%
  88 +\contentsline {subsubsection}{\numberline {7.4.2.1}Système de recommandation avec un jeu de données d'étudiants réels (TS avec Hawkes)}{98}{subsubsection.7.4.2.1}%
  89 +\contentsline {subsubsection}{\numberline {7.4.2.2}Mesures de performances}{100}{subsubsection.7.4.2.2}%
  90 +\contentsline {subsection}{\numberline {7.4.3}Conclusion}{100}{subsection.7.4.3}%
  91 +\contentsline {chapter}{\numberline {8}Conclusions et Perspectives}{103}{chapter.8}%
  92 +\contentsline {section}{\numberline {8.1}Conclusion générale}{103}{section.8.1}%
  93 +\contentsline {section}{\numberline {8.2}Perspectives}{105}{section.8.2}%
  94 +\contentsline {chapter}{\numberline {9}Publications}{107}{chapter.9}%
  95 +\contentsline {section}{\numberline {9.1}Publications liées au sujet de thèse}{107}{section.9.1}%
  96 +\contentsline {section}{\numberline {9.2}Autres publications}{108}{section.9.2}%