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chapters/contexte2.aux
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| 354 | 354 | |
| 355 | 355 | \section{Plan de la thèse} |
| 356 | 356 | |
| 357 | -Ce manuscrit est scindé en deux grandes parties. La première partie contient trois chapitres et la seconde en contient quatre. Le premier chapitre de la première partie (chapitre 2) vise à introduire le sujet, les concepts, les algorithmes associés, présenter le contexte général et l'application AI-VT initiale. Le deuxième chapitre de cette partie présente différents travaux emblématiques menés dans le domaine des environnements informatiques pour l'apprentissage humain. Le chapitre suivant conclut cette première partie en présentant le raisonnement à partir de cas. | |
| 357 | +Ce manuscrit est scindé en deux grandes parties de trois chapitres chacune. Le premier chapitre de la première partie (chapitre 2) vise à introduire le sujet, les concepts, les algorithmes associés, présenter le contexte général et l'application AI-VT initiale. Le deuxième chapitre de cette partie présente différents travaux emblématiques menés dans le domaine des environnements informatiques pour l'apprentissage humain. Le chapitre suivant conclut cette première partie en présentant le raisonnement à partir de cas. | |
| 358 | 358 | |
| 359 | -Dans la seconde partie du manuscrit, le chapitre 5 explicite l'architecture proposée pour intégrer les modules développés autour du système AI-VT initial et élargir ses fonctionnalités. Le chapitre 6 propose deux outils originaux fondés sur le raisonnement à partir de cas et les systèmes multi-agents pour résoudre des problèmes de régression de façon générique. Le chapitre 7 présente l'application de ces nouveaux outils de régression dans un système de recommandation intégré à AI-VT utilisant le raisonnement Bayésien. Ce chapitre montre de quelle manière il permet de réviser une séance d'entraînement en cours. Le chapitre 8 montre l'utilisation du processus de Hawkes pour renforcer l'apprentissage. | |
| 359 | +Dans la seconde partie du manuscrit, le chapitre 5 explicite l'architecture proposée pour intégrer les modules développés autour du système AI-VT initial et élargir ses fonctionnalités. Le chapitre 6 propose deux outils originaux fondés sur le raisonnement à partir de cas et les systèmes multi-agents pour résoudre des problèmes de régression de façon générique. Le chapitre 7 présente l'application de ces nouveaux outils de régression dans un système de recommandation intégré à AI-VT utilisant le raisonnement bayésien et le processus de Hawkes pour renforcer l'apprentissage. Ce chapitre montre de quelle manière il permet de réviser une séance d'entraînement en cours. | |
| 360 | 360 | |
| 361 | 361 | Enfin, une conclusion générale incluant des perspectives de recherche termine ce manuscrit. |
| 362 | 362 | |
| 363 | -\include{./chapters/contexte2.tex} | |
| 364 | - | |
| 365 | 363 | \part{État de l'art} |
| 364 | + | |
| 365 | +\include{./chapters/contexte2.tex} | |
| 366 | 366 | |
| 367 | 367 | \include{./chapters/EIAH.tex} |
| 368 | 368 |
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| ... | ... | @@ -3,28 +3,28 @@ |
| 3 | 3 | \contentsline {chapter}{\numberline {1}Introduction}{3}{chapter.1}% |
| 4 | 4 | \contentsline {section}{\numberline {1.1}Contributions Principales}{4}{section.1.1}% |
| 5 | 5 | \contentsline {section}{\numberline {1.2}Plan de la thèse}{5}{section.1.2}% |
| 6 | -\contentsline {chapter}{\numberline {2}Contexte}{7}{chapter.2}% | |
| 7 | -\contentsline {section}{\numberline {2.1}Les stratégies d'apprentissage humain et les environnements informatiques pour l'apprentissage humain (EIAH)}{7}{section.2.1}% | |
| 8 | -\contentsline {subsection}{\numberline {2.1.1}Les stratégies d'apprentissage}{7}{subsection.2.1.1}% | |
| 9 | -\contentsline {subsection}{\numberline {2.1.2}Les EIAH}{8}{subsection.2.1.2}% | |
| 10 | -\contentsline {subsection}{\numberline {2.1.3}L'exerciseur initial AI-VT}{9}{subsection.2.1.3}% | |
| 11 | -\contentsline {section}{\numberline {2.2}Le contexte technique}{10}{section.2.2}% | |
| 12 | -\contentsline {subsection}{\numberline {2.2.1}Le raisonnement à partir de cas (RàPC)}{10}{subsection.2.2.1}% | |
| 13 | -\contentsline {subsubsection}{\numberline {2.2.1.1}Retrouver (Rechercher)}{12}{subsubsection.2.2.1.1}% | |
| 14 | -\contentsline {subsubsection}{\numberline {2.2.1.2}Réutiliser (Adapter)}{12}{subsubsection.2.2.1.2}% | |
| 15 | -\contentsline {subsubsection}{\numberline {2.2.1.3}Réviser et Réparer}{12}{subsubsection.2.2.1.3}% | |
| 16 | -\contentsline {subsubsection}{\numberline {2.2.1.4}Retenir (Stocker)}{13}{subsubsection.2.2.1.4}% | |
| 17 | -\contentsline {subsubsection}{\numberline {2.2.1.5}Conteneurs de Connaissance}{14}{subsubsection.2.2.1.5}% | |
| 18 | -\contentsline {subsection}{\numberline {2.2.2}Les systèmes multi-agents}{14}{subsection.2.2.2}% | |
| 19 | -\contentsline {subsection}{\numberline {2.2.3}Différents algorithmes et fonctions implémentés dans AI-VT pour la personnalisation et l'adaptation des séances d'entrainement proposées}{15}{subsection.2.2.3}% | |
| 20 | -\contentsline {subsubsection}{\numberline {2.2.3.1}Pensée Bayesienne}{15}{subsubsection.2.2.3.1}% | |
| 21 | -\contentsline {subsubsection}{\numberline {2.2.3.2}Méthode des k plus proches voisins (K-Nearest Neighborhood - KNN)}{16}{subsubsection.2.2.3.2}% | |
| 22 | -\contentsline {subsubsection}{\numberline {2.2.3.3}K-Moyennes}{17}{subsubsection.2.2.3.3}% | |
| 23 | -\contentsline {subsubsection}{\numberline {2.2.3.4}Modèle de Mélange Gaussien GMM (\textit {Gaussian Mixture Model})}{18}{subsubsection.2.2.3.4}% | |
| 24 | -\contentsline {subsubsection}{\numberline {2.2.3.5}Fuzzy-C}{18}{subsubsection.2.2.3.5}% | |
| 25 | -\contentsline {subsubsection}{\numberline {2.2.3.6}Bandit Manchot MAB (\textit {Multi-Armed Bandits})}{19}{subsubsection.2.2.3.6}% | |
| 26 | -\contentsline {subsubsection}{\numberline {2.2.3.7}Échantillonnage de Thompson TS (\textit {Thompson Sampling})}{19}{subsubsection.2.2.3.7}% | |
| 27 | -\contentsline {part}{II\hspace {1em}État de l'art}{21}{part.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}{16}{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'entrainement 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 | 28 | \contentsline {chapter}{\numberline {3}Environnements Informatiques d'Apprentissage Humain}{23}{chapter.3}% |
| 29 | 29 | \contentsline {section}{\numberline {3.1}L'Intelligence Artificielle}{23}{section.3.1}% |
| 30 | 30 | \contentsline {section}{\numberline {3.2}Systèmes de recommandation dans les EIAH}{24}{section.3.2}% |