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Il s'agit d'une technique d'intelligence artificielle dont l'idée est de résoudre un nouveau problème grâce aux connaissances déjà acquises par le système et en tenant un raisonnement fondé sur l'analogie. Le RàPC est apparu comme une alternative pour améliorer les systèmes experts. Shank et Abelson \cite{schank+abelson77} ont initialement mené des travaux sur l'organisation hiérarchique de la mémoire pour imiter le raisonnement humain. Ceux-ci ont servi de fondement aux travaux de Janet Kolodner \cite{KOLODNER1983281} et ont abouti à l'implémentation un système fondé sur ces principes en 1983. Le terme \textit{raisonnement à partir de cas} est utilisé pour la première fois en 1989 par Riesbeck et Shank \cite{Riesbeck1989}. - -Comme vu dans le chapitre du contexte, le raisonnement à partir de cas utilise quatre conteneurs de connaissance pour représenter la connaissance complète du système, chaque conteneur stocke l'information associée à une fonction spécifique et tout est fondé sur le carré d'analogie, c'est-à-dire des solutions ayant permis de résoudre un problème ancien sont réutilisées afin de résoudre un problème nouveau similaire. La plupart des systèmes de RàPC utilisent pour son fonctionnement comme base un cycle composé de quatre étapes (retrouver, réutiliser, réviser et retenir). - -Les travaux ici cités sont des travaux représentatifs parce que ils ont permis de trouver certaines points d'amélioration du RàPC, ont donné des pistes d'intégration avec d'autres techniques ou ont donné des idées de modification pour obtenir bons résultats en gagnant de la performance. - -Plusieurs travaux combinent le RàPC avec les réseaux de neurones (Deep Learning) avec l'objectif d'améliorer les réponses générées et optimiser les performances de chaque algorithme. C'est une idée qui marche dans certains cas, mais qui n'est pas très récente comme par exemple dans \cite{JUNG20095695} les auteurs développent un système fondé sur l'hybridation du RàPC avec des réseaux de neurones pour concevoir des produits. Le système se focalise uniquement dans les phases "rechercher" et "réutiliser" du RàPC dans lesquelles sont exécutés les algorithmes implémentés. Le système détermine de façon automatique les valeurs pour les paramètres nécessaires à la conception d'un produit particulier en suivant le cycle traditionnel du RàPC. Avec l'algorithme de k-moyennes, est extrait un cas représentatif de la base de cas et l'adaptation des solutions des voisins trouvées est faite avec le réseau de neurones RBFN (Radial Basis Function Network). L'évaluation du système est faite en utilisant une base de données contenant 830 tests, les résultats démontrent que les produits conçus s'ajustent avec un grand pourcentage aux normes définies. - -Dans \cite{10.1007/978-3-642-15973-2_50} un réseau de neurones classique est implémenté pour définir la géométrie d'une matrice pour l'extrusion de l'aluminium. En effet, actuellement c'est un processus qui se fait manuellement et par essai et erreur. Le RàPC est alors utilisé pour aider à déterminer les valeurs optimales des paramètres du réseau, en utilisant l'information des matrices d'extrusion déjà testées. - -Aussi le travail de \cite{PETROVIC201617} où les auteurs proposent un système de raisonnement à partir de cas pour calculer la dose optimale de radiation pour le traitement du cancer. Dans ce domaine particulier de la radiothérapie, administrer une dose nécessite de connaître avec précision le nombre de faisceaux et l'angle de chacun d'eux. L'algorithme proposé tente de trouver la combinaison de valeurs optimales pour ces deux paramètres en utilisant les réseaux de neurones. L'utilisation des réseaux de neurones intervient lors de l'adaptation des cas connus : ils modifient le nombre et les angles des faisceaux. La validation de l'algorithme est évaluée avec une base de 80 cas réels de cancer du cerveau extraits de l'hôpital de Nottingham City. Le nombre de neurones et de couches ont été définis de façon empirique. Les résultats montrent que l'utilisation des cas historiques et la construction des solutions à partir des solutions déjà connues permet une amélioration de 12\% concernant la décision du nombre de faisceaux et de 29\% concernant la décision liée à leur angle. - -Plus récemment se trouvent travaux comme \cite{wolf2024keep} où le RàPC est employée comme technique pour expliquer les résultats générés par un réseau qui classifie les images en fonction de certains attributs et zones dans les images, le résultats permettent conclure que les explications trouvées avec RàPC sont très fidèles aux images testées. Aussi \cite{PAREJASLLANOVARCED2024111469} utilisent le RàPC et ses avantages pour le coupler à un réseau profond (Deep Learing) pour sélectionner la meilleure explication au résultat donné par le classificateur d'images dans ce cas, la base de connaissance sont certaines images avec des étiquettes qui contiennent l'information associée à l'image, la comparaison réalisée avec les algorithmes représentatifs de l'état de l'art suggèrent que la combinaison Deep Learning marche bien dans ce type de problèmes. - -Certains travaux ont appliqué le raisonnement à partir de cas à un problème spécifique en modifiant les représentations des cas et des solutions, d'autres ont modifié le cycle conceptuel comme le montre la figure \figref{figMCBR2} avec l'objectif d'obtenir des réponses dynamiques et plus aptes à problèmes complexes. Dans \cite{10.1007/978-3-319-47096-2_11} est proposé un cycle complémentaire incluant un outil d'apprentissage profond (\textit{Deep Learning}) pour améliorer le résultat du processus du RàPC. Dans \cite{10.1007/978-3-642-15973-2_50}, la phase de stockage est modifiée en retenant les cas dont les résultats n'ont pas eu de succès, pour guider le processus dans la fabrications de nouvelles pièces. Enfin, dans \cite{Robertson2014ARO}, les auteurs proposent d'ajouter à chaque cas une valeur d'utilité espérée selon chaque action possible. Cet ajout s'accompagne d'une prédiction probabiliste des actions que l'application engendrera en réponse. Cette prédiction probabiliste dépend bien entendu de l'état initial du système avant mise en oeuvre de l'action. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR2.png}{Ajout d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (Traduit de \cite{10.1007/978-3-319-47096-2_11})}{figMCBR2} - -Plusieurs travaux appliquent le RàPC avec succès en proposant des modifications dans chacune des phases ou en combinant différents algorithmes. Certains systèmes de RàPC appliqués au domaine de la conception de produits sont remarquables à ce titre. Dans \cite{ROLDANREYES20151} les auteurs proposent, comme le montre la figure \figref{figMCBR1}, un algorithme pour produire le propylène glycol dans un réacteur chimique. Dans ce cas, la phase de réutilisation du RàPC est couplée à la recherche des états qui satisfassent le nouveau problème (\textit{Constraint satisfaction problems CSP}) en utilisant l'information des cas de base déjà résolus. Les solutions trouvées sont évaluées selon le nombre de changements réalisés sur les solutions déjà connues (parcimonie), le nombre de solutions possibles trouvées (précision), l'évaluation de commentaires faits par des experts et la complexité des transformations réalisées. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR1.png}{Cycle du RàPC modifié. (Traduit de \cite{ROLDANREYES20151})}{figMCBR1} - -Dans \cite{10.1007/978-3-319-47096-2_11}, les auteurs ajoutent un nouveau cycle au cycle traditionnel du RàPC. Le premier cycle génère des descriptions abstraites du problème avec un réseau de neurones et des algorithmes génétiques; le second cycle prend les descriptions abstraites comme des nouveaux cas, cherche les cas similaires et adapte les solutions rencontrées. L'exécution des deux cycles prend en compte certains critères prédéfinis par l'utilisateur. En comparant le même problème avec le cycle traditionnel du RàPC, les auteurs mesurent une amélioration de la qualité des recettes proposées et montrent que celles-ci sont plus en accord avec les critères définis. - -\cite{10.1007/978-3-319-61030-6_1} s'intéressent quant à eux à la génération de recettes de cuisine originales. Les auteurs modifient le cycle traditionnel du RàPC et créent deux cycles, combinant l'apprentissage profond et la récupération de cas basée sur la similarité, le premier cycle génère des descriptions abstraites de la conception avec des algorithmes génétiques et un réseau neuronal profond, le second cycle utilise les descriptions abstraites pour récupérer et adapter des objets, cette structure donne lieu à un prototype appelé Q-chef qui génère une recette basée sur la base de données d'ingrédients et les demandes de l'utilisateur. Ce travail ne montre pas de métriques standard génériques mais utilise deux nombres indicatifs (plausibilité et surprise) pour démontrer la génération efficace de nouvelles recettes selon les critères de l'utilisateur en comparant le RàPC à deux cycles avec le RàPC à un cycle, démontrant plus de plausibilité et de surprise dans les recettes générées. - -Par rapport à l'intégration du RàPC avec d'autres algorithmes nous pouvons citer \cite{buildings13030651} qui mettent en Å“uvre un RáPC avec une méthode d'agrégation bootstrap (bagging) pour améliorer la précision du RàPC lorsque il n'y a pas suffisamment de cas et réduire la variance. Le problème qui ils essaient de résoudre est l'estimation des coûts conceptuels dans les décisions de faisabilité d'un projet, lorsque l'on ne dispose pas de suffisamment d'informations sur la conception détaillée et les exigences du projet. Les résultats ont révélé que la performance de prédiction de la méthode RàPC agrégée bootstrap est meilleure que la performance de prédiction de la méthode RàPC traditionnelle. - -Un modèle d'ensemble fondé sur le raisonnement à partir de cas est proposé par \cite{YU2023110163} appliqué à la prédiction financière et au remplissage des données manquantes. Dans ce cas, pour retrouver les plus proches voisins, le modèle utilise trois mesures de distance différentes et une étape de vote pour l'intégration. Le modèle a été testé avec une base de données comportant onze dimensions d'informations financières provenant de 249 entreprises. La comparaison est faite avec deux objectifs. Premièrement, le remplissage des données manquantes avec d'autres algorithmes tels que KNN ou RandomForest, et deuxièmement, la comparaison de la prédiction avec des algorithmes uniques utilisant une métrique de distance spécifique. En effet, les résultats montrent une meilleure performance dans le remplissage des données manquantes et les meilleurs résultats dans la prédiction. - -La représentation des cas peut permettre également d'améliorer les résultats d'un système de RàPC. La performance d'un système de RàPC dépend de la quantité d'informations stockées, mais également des algorithmes implémentés. C'est le cas dans \cite{Muller} où les recettes de cuisine sont codées comme un processus de transformation et mélangent des ingrédients en suivant une suite d'étapes ordonnés. Pour créer des recettes innovantes, une mesure de distance est utilisée entre les ingrédients. Cette mesure permet de trouver une recette en substituant certains ingrédients par d'autres, similaires ou aux qualités et/ou caractéristiques similaires. De la même manière, il est possible de créer des recettes plus proches des exigences des utilisateurs. Les étapes de transformation appelées aussi opérateurs sont stockées et catégorisées grâce à une métrique permettant de les échanger afin d'obtenir une nouvelle recette. - -La génération, analyse et correction de texte constitue également un domaine d'application intéressant du RàPC. Pour la réalisation de ces tâches il est parfois nécessaire de transformer le texte en représentation numérique ou de mesurer la proximité sémantique de mots. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit explore les transformations possibles afin que la nouvelle histoire ne soit pas très similaire aux histoires déjà connues mais que elle soit cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de celle-ci déterminent si l'histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit recommencer; l'adaptation se fait en recherchant les personnages, les contextes, les objets dans la base de cas, et en les unifiant avec des actions; la plupart des histoires générées sont cohérentes mais elles sont constituées de paragraphes très courts. - -Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées. Ce travail n'utilise ni la transformation numérique des phrases, ni de connaissances linguistiques, mais retrouve les phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant une mesure de distance avec les phrases correctes et incorrectes de la base de connaissances. Si les phrases similaires sont incorrectes, le système peut proposer une correction en changeant certains mots selon le contexte et recalculer les distances afin de mesurer la cohérence et pertinence de la phrase proposée. - -Une étape très importante dans le RàPC est l'adaptation. Dans \cite{10.1007/978-3-031-63646-2_4} l'objectif est changer la représentation de la connaissance pour évaluer s'il y a un impact positif en les solutions générées, ce changement permet aussi d'établir règles de adaptation, les résultats permettent de dire que choisir une représentation adéquate et des règles correctes en fonction du problème étudié peut améliorer la qualité des solutions pour résoudre des problèmes complexes. - -Étant donne la versatilité du RàPC dans la littérature se trouvent des travaux qui l'utilisent comme un outil de prédiction, car en connaissant l'information historique généralement est possible d'inférer la structure ou le comportement des données et avec le RàPC les résultats sont assez bons. - -L'objectif du travail de \cite{10.1007/978-3-030-01081-2_25} est la prédiction du temps de course pour un athlète. L'algorithme KNN est utilisé pour rechercher les cas similaires. Le système interpole un temps final grâce au calcul d'une moyenne pondérée des meilleurs temps des cas similaires retrouvés dans la première étape du RàPC. - -Dans \cite{10.1007/978-3-030-58342-2_8}, les auteurs essaient de prédire le meilleur temps d'un patineur par analogie avec ceux des patineurs ayant des caractéristiques et une histoire de course similaires. Cependant parfois, calculer une moyenne des temps similaires trouvés ne suffit pas. Certaines caractéristiques liées au contexte, à l'environnement et à la nature de la course (le type de course, le type de piste, la distance à parcourir, etc.), peuvent en effet influencer de manière importante la performance du patineur. L'algorithme a été testé avec une base de données contenant les informations de 21 courses de 500m, 700m, 1000m, 1500m, 3km, 5km and 10km réalisées entre Septembre 2015 et Janvier 2020. - -Un système multi-fonctionnel est décrit dans \cite{10.1007/978-3-030-58342-2_5}. Celui-ci permet d'obtenir une prédiction du temps de course, de suggérer un plan du rythme de la course et il recommande également un plan d'entraînement pour une course donnée. Les trois fonctionnalités sont implémentées en utilisant le RàPC. Les calculs de la similarité sont fondés sur un historique et des caractéristiques physiques des coureurs. Les plans d'entraînement sont génériques et sont proposés sur les 16 semaines précédant le début du marathon ciblé (selon les auteurs, c'est en effet le temps usuel pour une préparation à ce type d'épreuve). Le système a été évalué avec une base de données constituée de caractéristiques de 21000 coureurs des marathons de Dublin, Londres ou New-York pour la période de 2014 à 2017. - -Dans \cite{YU2024123745} est proposé un algorithme en deux stages pour prédire avec précision et robustesse la détresse financière, les deux stages utilisent le RàPC. Le premier stage permet de compléter les valeurs manquantes de la base de données choisie afin de obtenir une meilleure performance fiable et stable, après est exécutée une version du RàPC combinée avec un LVQ (Learning Vector Quantization) algorithme pour faire la prédiction de classification. Les résultats démontrent compétitive solutions pour compléter les valeurs manquantes et classification. - -\cite{Sadeghi} présentent un nouvel algorithme pour la prévision de la demande de matériel de secours utilisant le RàPC avec la méthode du meilleur-pire (Best-Worst Method - BWM) et les modèles de Markov cachés (Hidden Markov Model - HMM). Le HMM est entrainé avec des données historiques de demande de matériel, une fois l'algorithme détecte une situation de catastrophe, le RàPC cherche les cas similaires pour proposer le matériel nécessaire. D'après les résultats, l'indice d'erreur de prévision est inférieur à 10\%, par conséquent, le CBR-BWM-HMM proposé est un algorithme solide et robuste. - -Les systèmes de recommandation et le RàPC peuvent aussi être combinés comme dans le système proposé par \cite{8495930} montre les bénéfices de l'utilisation du RàPC dans les environnements informatiques pour l'apprentissage humain (EIAH). Le modèle proposé suit le cycle traditionnel du RàPC en combinant les modèles d'apprentissages traditionnels et numériques. Les principales contributions sont la représentation des cas et la recommandation des parcours d'apprentissage personnalisés selon les informations issues des autres apprenants. Une base de cas initiaux a été créée pour mesurer l'efficacité du modèle. Celle-ci stocke la recommandation du parcours de 120 apprenants. Des examens sont réalisés avant et après avoir suivi le parcours recommandé par le système permettent de mesurer l'efficacité de la recommandation proposée. - -\cite{Obeid}. Le système décrit dans cette étude présente la particularité d'être capable d'analyser des données hétérogènes et multidimensionnelles. Dans ce travail, un parcours de carrière et les universités/collèges est recommandé aux élèves du secondaire en fonction de leurs intérêts. Ce travail montre également une taxonomie des techniques algorithmiques généralement utilisées dans les systèmes de recommandation pour les EIAH (figure \figref{figTax}). - -En fonction de certains paramètres comme type d'école, nombre d'étudiants, nombre d'étudiants admis, nombre d'étudiants non-admis, total nombre de classes, etc. -\cite{skittou2024recommender} développent un modèle hybride entre RàPC et le raisonnement à partir de règles pour recommander des actions de planification de l'éducation en réponse à des cas éducatifs des écoles primaires. Le modèle a été testé des bases de données réelles et a donné de bons résultats avec une grande précision. - -%Le RàPC a également été mis en oeuvre dans différents travaux liés à la génération, l’analyse et la correction de textes écrits. Pour la réalisation de ces tâches, il est parfois nécessaire de transformer le texte en représentation numérique ou d’établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit dans ce travail explore les transformations de forme possibles afin que la nouvelle histoire ne soit pas similaire aux histoires déjà connues tout en étant cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si l’histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit être recommencé. L'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en \colorbox{pink}{unifiant tout avec des actions ??}. Les résultats montrent que la plupart des histoires générées sont cohérentes mais qu'elles sont constituées de paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20}, les phrases écrites en langue française sont corrigées, ce travail n'utilise ni la transformation numérique \colorbox{yellow}{?} des phrases, ni des connaissances linguistiques. Le système conçu retrouve des phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant la distance entre toutes les phrases \colorbox{yellow}{lesquelles ?} pour savoir \colorbox{pink}{je ne comprends pas la suite }si elle est bien écrite ou pas. Si la phrase n’est pas bien écrite, le système peut proposer une correction en changeant certains mots selon le contexte et calculer à nouveau les distances mesurant la cohérence et pertinence de la phrase. - -\mfigure[!ht]{width=\textwidth}{./Figures/taxonomieEIAH.png}{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (Traduit de \cite{Obeid})}{figTax} - -%\begin{figure} -%\centering -%\includegraphics[scale=21]{./Figures/taxonomieEIAH.png} -%\caption{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (\cite{Obeid})} -%\label{figTax} -%\end{figure} - -Le tableau \ref{tabArts2} montre un récapitulatif des articles analysés dans cet état de l'art du RàPC, où on montre chacun d'eux en ordre d'apparition dans le chapitre et les limitations trouvées par rapport aux données, flexibilité, généralité de l'algorithme proposé, validité des solutions proposées, automatisation du processus et complexité du modèle proposée. A part les limites identifiés dans le tableau, nous avons trouvé deux problèmes très communs des systèmes de recommandation de façon générale : (1)le \textit{cold-start} qui se produit au début d'une recommandation lorsque il n'y a pas suffisamment de données pour calculer ou inférer une recommandation appropriée \cite{HU2025127130}. Et (2) le \textit{gray-sheep} qui se produit lorsqu'un utilisateur présente un comportement très différent de ceux qui sont stockés dans la base de données. Le système ne peut donc pas générer des recommandations en se basant sur l'information disponible \cite{ALABDULRAHMAN2021114061}. - -\begin{table} -\footnotesize -\begin{tabular}{p{4.4cm}p{9cm}} -Ref&Limites\\ -\hline - -\cite{JUNG20095695}&Le modèle d'adaptation proposé fonctionne seulement avec des cas très proches. L'apprentissage dans le RàCP se limite à stocker les nouveaux cas.\\ - -\cite{10.1007/978-3-642-15973-2_50}&Le RàPC n'est pas modifié ou amélioré. La révision dans le RàPC n'est pas automatique\\ - -\cite{PETROVIC201617}&Grande quantité de données pour que l'algorithme fonctionne correctement. Recommandation limitée à un problème très spécifique.\\ - -\cite{wolf2024keep}&Le modèle n'est actuellement appliqué qu'à la classification d'images à un seul label \\ - -\cite{PAREJASLLANOVARCED2024111469}&La base de données de test a été construite avec des évaluations subjectives. Le modèle ne considère pas les incertitudes.\\ - -\cite{ROLDANREYES20151}&Une seule méthode d'adaptation est utilisée. Le modèle n'exploite pas les cas qui présentent une erreur.\\ - -\cite{10.1007/978-3-319-47096-2_11}&Beaucoup de données sont nécessaires pour entrainer le modèle de 'Deep Learning'. Les solutions générées n'ont pas été validées.\\ - -\cite{10.1007/978-3-319-61030-6_1}&L'évaluation des solutions générées n'est pas automatique. Les solutions sont produites avec un seul point de vue.\\ - -\cite{buildings13030651}&La fonction d'unification des solutions est une moyenne des solutions générées avec la division des données et un seul approche\\ - -\cite{YU2023110163}&Une base de données déséquilibrée peut affecter négativement la performance du modèle proposé\\ - -\cite{Muller}&Une seule approche pour adapter les cas dans le RàPC. La révision dans le RàPC n'est pas automatique.\\ - -\cite{10.1007/978-3-319-24586-7_20}&Les solutions générées peuvent présenter des incohérences. Il n'y a pas une métrique objective pour mesurer la qualité des réponses.\\ - -\cite{10.1007/978-3-030-58342-2_20}&Les résultats ne sont pas très bons. Le modèle n'a pas de connaissances linguistiques.\\ - -\cite{10.1007/978-3-031-63646-2_4}&Les règles d'adaptation définies sont fixes et peuvent être limitées pour certaines situations\\ - -\cite{10.1007/978-3-030-01081-2_25}&Les prédictions n'ont pas été testées dans le monde réel. Les données sont très spécifiques et peu variées.\\ - -\cite{10.1007/978-3-030-58342-2_8}&Les prédictions sont pour des cas limités. Le modèle est entrainé pour un type d'utilisateur spécifique.\\ - -\cite{10.1007/978-3-030-58342-2_5}&Seulement une technique de sélection de solutions. Les données nécessaires pour le modèle sont complexes.\\ - -\cite{YU2024123745}&Il n'y a pas de révision des cas dans la méthode d'imputation ni dans le modèle de classification fondé sur RàPC. Cela peut accroître le biais d'imputation et introduire des échantillons avec bruit\\ - -\cite{Sadeghi}&Le temps de calcul peut être très grand en fonction de la quantité de données associées, du a l'étape d'entrainement du modèle HMM\\ - -\cite{8495930}&Le modèle a été validé avec peu de données. Les recommandations se basent seulement sur l'information des autres apprenants.\\ - -\cite{Obeid}&L'ontologie peut être limitée pour évaluer plusieurs cas inconnus ou imprévus. Il est nécessaire d'avoir une forte connaissance du domaine.\\ - -\cite{skittou2024recommender}&Les règles de décision établies sont fixes et avec l'hybridation peut avoir de l'incertitude et l'imprécision\\ - -\end{tabular} -\caption{Tableau de synthèse des articles analysés dans l’état de l’art du RàPC} -\label{tabArts2} -\end{table} - -%\begin{figure} -%\centering -%\includegraphics[scale=0.4]{./Figures/ModCBR2.png} -%\caption{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (\cite{10.1007/978-3-319-47096-2_11})} -%\label{figMCBR2} -%\end{figure} +\chapter{\'Etat de l'art (Raisonnement à Partir de Cas)} + +Le raisonnement à partir de cas est une approche fondée sur la connaissance. Il s'agit d'une technique d'intelligence artificielle dont l'idée est de résoudre un nouveau problème grâce aux connaissances déjà acquises par le système et en tenant un raisonnement fondé sur l'analogie. Le RàPC est apparu comme une alternative pour améliorer les systèmes experts. Shank et Abelson \cite{schank+abelson77} ont initialement mené des travaux sur l'organisation hiérarchique de la mémoire pour imiter le raisonnement humain. Ceux-ci ont servi de fondement aux travaux de Janet Kolodner \cite{KOLODNER1983281} et ont abouti à l'implémentation un système fondé sur ces principes en 1983. Le terme \textit{raisonnement à partir de cas} est utilisé pour la première fois en 1989 par Riesbeck et Shank \cite{Riesbeck1989}. + +Comme vu dans le chapitre du contexte, le raisonnement à partir de cas utilise quatre conteneurs de connaissance pour représenter la connaissance complète du système. Chaque conteneur stocke l'information associée à une fonction spécifique et tout est fondé sur le carré d'analogie : des solutions ayant permis de résoudre un problème sont réutilisées afin de résoudre un nouveau problème similaire. La plupart des systèmes de RàPC fonctionnent suivant un cycle composé de quatre étapes : retrouver, réutiliser, réviser et retenir. + +Les travaux présentés dans ce chapitre ont nouri notre réflexion et ont ouvert des perspectives d'amélioration de performance du système AI-VT. Ce chapitre commence par la présentation de travaux emblématiques intégrant un réseau de neurones au cycle de RàPC. Cette première section est suivie d'une présentation de différents travaux liés à l'explicabilité, toujours en utilisant le RàPC. Des travaux récents sur la représentation des cas et le cycle du RàPC, puis sur l'intégration d'autres algorithmes au cycle du RàPC sont ensuite présentés. Pour finir, la prédiction et la recommandation via RàPC terminent ce chapitre. + +\section{Intégration d'un réseau de neurones dans le cycle du RàPC} + +Plusieurs travaux combinent le RàPC avec les réseaux de neurones avec l'objectif d'améliorer les réponses générées et optimiser les performances de chaque algorithme. C'est une idée explorée dès 2009 par Jung \textit{et al.} \cite{JUNG20095695}. Les auteurs de cet article ont en effet développé un système fondé sur l'hybridation du RàPC avec des réseaux de neurones pour concevoir des produits. Cette hybridation est iméplementée dans les phases "rechercher" et "réutiliser" : le système détermine de façon automatique les valeurs pour les paramètres nécessaires à la conception d'un produit particulier en suivant le cycle traditionnel du RàPC. Avec l'algorithme de k-moyennes, est extrait un cas représentatif de la base de cas et l'adaptation des solutions des voisins trouvées est faite avec le réseau de neurones RBFN (\textit{Radial Basis Function Network}). L'évaluation du système est faite en utilisant une base de données contenant 830 tests, les résultats démontrent que les produits conçus s'ajustent avec un grand pourcentage aux normes définies. + +Dans \cite{10.1007/978-3-642-15973-2_50} un réseau de neurones classique est implémenté pour définir la géométrie d'une matrice pour l'extrusion de l'aluminium. En effet, actuellement c'est un processus qui se fait manuellement et par essai et erreur. Le RàPC est alors utilisé pour aider à déterminer les valeurs optimales des paramètres du réseau, en utilisant l'information des matrices d'extrusion déjà testées. + +Petrovic \textit{et al.} \cite{PETROVIC201617} proposent un système de raisonnement à partir de cas pour calculer la dose optimale de radiation pour le traitement du cancer. Dans ce domaine particulier de la radiothérapie, administrer une dose nécessite de connaître avec précision le nombre de faisceaux et l'angle de chacun d'eux. L'algorithme proposé tente de trouver la combinaison de valeurs optimales pour ces deux paramètres en utilisant les réseaux de neurones. L'utilisation des réseaux de neurones intervient lors de l'adaptation des cas connus : ils modifient le nombre et les angles des faisceaux. La validation de l'algorithme est évaluée avec une base de 80 cas réels de cancer du cerveau extraits de l'hôpital de Nottingham City. Le nombre de neurones et de couches ont été définis de façon empirique. Les résultats montrent que l'utilisation des cas historiques et la construction des solutions à partir des solutions déjà connues permet une amélioration de 12\% concernant la décision du nombre de faisceaux et de 29\% concernant la décision liée à leur angle. + +\section{Le RàPC pour construire ou corriger un texte, pour consolider ou expliquer les résultats obtenus} + +La génération, analyse et correction de texte constitue également un domaine d'application intéressant du RàPC. Pour la réalisation de ces tâches il est parfois nécessaire de transformer le texte en représentation numérique ou de mesurer la proximité sémantique de mots. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit explore les transformations possibles afin que la nouvelle histoire ne soit pas très similaire aux histoires déjà connues mais que elle soit cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de celle-ci déterminent si l'histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit recommencer; l'adaptation se fait en recherchant les personnages, les contextes, les objets dans la base de cas, et en les unifiant avec des actions; la plupart des histoires générées sont cohérentes mais elles sont constituées de paragraphes très courts. + +Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées. Ce travail n'utilise ni la transformation numérique des phrases, ni de connaissances linguistiques, mais retrouve les phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant une mesure de distance avec les phrases correctes et incorrectes de la base de connaissances. Si les phrases similaires sont incorrectes, le système peut proposer une correction en changeant certains mots selon le contexte et recalculer les distances afin de mesurer la cohérence et pertinence de la phrase proposée. + +Plus récemment Wolf \textit{et al.} \cite{wolf2024keep} ont présenté un système dans lequel le RàPC est employé pour expliquer les résultats générés par un réseau qui classifie les images en fonction de certains attributs et zones dans les images. Les résultats obtenus permettent de conclure que le RàPC permet d'expliquer fidèlement le contenu des images testées. + +\cite{PAREJASLLANOVARCED2024111469} utilisent également le RàPC pour sélectionner la meilleure explication au résultat donné par le classificateur d'images fondé sur l'apprentissage profond (\textit{Deep Learning}). Dans ce système, la base de connaissances est constituée d'images avec des étiquettes décrivant l'information associée. Les résultats obtenus par ce système sont très prometteurs. + +\section{Travaux récents sur la représentation des cas et le cycle du RàPC} + +Certains travaux ont appliqué le raisonnement à partir de cas à un problème spécifique en modifiant les représentations des cas et des solutions. D'autres ont modifié le cycle conceptuel comme le montre la figure \figref{figMCBR2} avec l'objectif d'obtenir des réponses dynamiques et plus aptes à proposer des solutions à des problèmes complexes. + +En effet, la représentation des cas peut permettre d'améliorer les résultats d'un système de RàPC. La performance d'un système de RàPC dépend de la quantité d'informations stockées, mais également des algorithmes implémentés. C'est le cas dans \cite{Muller} où les recettes de cuisine sont codées comme un processus de transformation et mélangent des ingrédients en suivant une suite d'étapes ordonnés. Pour créer des recettes innovantes, une mesure de distance est utilisée entre les ingrédients. Cette mesure permet de trouver une recette en substituant certains ingrédients par d'autres, similaires ou aux qualités et/ou caractéristiques similaires. De la même manière, il est possible de créer des recettes plus proches des exigences des utilisateurs. Les étapes de transformation appelées aussi opérateurs sont stockées et catégorisées grâce à une métrique permettant de les échanger afin d'obtenir une nouvelle recette. + +Les auteurs de \cite{10.1007/978-3-319-47096-2_11} introduisent un cycle complémentaire incluant un outil d'apprentissage profond pour améliorer le résultat du système fondé sur le RàPC. + +Dans \cite{10.1007/978-3-642-15973-2_50}, la phase de stockage est modifiée en retenant les cas dont les résultats n'ont pas eu de succès, pour guider le processus dans la fabrications de nouvelles pièces. + +Enfin, dans \cite{Robertson2014ARO}, les auteurs proposent d'ajouter à chaque cas une valeur d'utilité espérée en fonction de chaque action possible. Cet ajout s'accompagne d'une prédiction probabiliste des actions que l'application engendrera en réponse. Cette prédiction probabiliste dépend bien entendu de l'état initial du système avant mise en oeuvre de l'action. + +\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR2.png}{Ajout d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (Traduit de \cite{10.1007/978-3-319-47096-2_11})}{figMCBR2} + +Plusieurs travaux appliquent le RàPC avec succès en proposant des modifications dans chacune des phases ou en combinant différents algorithmes. Certains systèmes de RàPC appliqués au domaine de la conception de produits sont remarquables à ce titre. + +Dans \cite{ROLDANREYES20151}, les auteurs proposent un algorithme pour produire le propylène glycol dans un réacteur chimique comme le montre la figure \figref{figMCBR1}. Dans ce cas, la phase de réutilisation du RàPC est couplée à la recherche des états satisfaisant le nouveau problème (\textit{Constraint satisfaction problems CSP}) en utilisant l'information des cas de base déjà résolus. Les solutions trouvées sont évaluées selon le nombre de changements réalisés sur les solutions déjà connues (parcimonie), le nombre de solutions possibles trouvées (précision), l'évaluation de commentaires faits par des experts et la complexité des transformations réalisées. + +\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR1.png}{Cycle du RàPC modifié. (Traduit de \cite{ROLDANREYES20151})}{figMCBR1} + +Dans \cite{10.1007/978-3-319-47096-2_11}, les auteurs ajoutent un nouveau cycle au cycle traditionnel du RàPC. Le premier cycle génère des descriptions abstraites du problème avec un réseau de neurones et des algorithmes génétiques; le second cycle prend les descriptions abstraites comme des nouveaux cas, cherche les cas similaires et adapte les solutions rencontrées. L'exécution des deux cycles prend en compte certains critères prédéfinis par l'utilisateur. En comparant le même problème avec le cycle traditionnel du RàPC, les auteurs mesurent une amélioration de la qualité des recettes proposées et montrent que celles-ci sont plus en accord avec les critères définis. + +\cite{10.1007/978-3-319-61030-6_1} s'intéressent quant à eux à la génération de recettes de cuisine originales. Les auteurs modifient le cycle traditionnel du RàPC et créent deux cycles, combinant l'apprentissage profond et la récupération de cas basée sur la similarité, le premier cycle génère des descriptions abstraites de la conception avec des algorithmes génétiques et un réseau neuronal profond, le second cycle utilise les descriptions abstraites pour récupérer et adapter des objets, cette structure donne lieu à un prototype appelé Q-chef qui génère une recette fondée sur d'autres ingrédients connus du système et les demandes de l'utilisateur. Ce travail ne montre pas de métriques standard génériques mais utilise deux nombres indicatifs (plausibilité et surprise) pour démontrer la génération efficace de nouvelles recettes selon les critères de l'utilisateur en comparant le RàPC à deux cycles avec le RàPC à un cycle, démontrant plus de plausibilité et de surprise dans les recettes générées. + +\section{Intégration d'autres algorithmes dans le cycle du RàPC} + +Par rapport à l'intégration du RàPC avec d'autres algorithmes nous pouvons citer \cite{buildings13030651} qui mettent en Å“uvre un RáPC avec une méthode d'agrégation \textit{bootstrap} (\textit{bagging}) pour améliorer la précision du RàPC lorsqu'il n'y a pas suffisamment de cas et réduire la variance. L'une des problématiques auxquelles les auteurs tentent de répondre dans cet article est l'estimation des coûts conceptuels dans les décisions de faisabilité d'un projet lorsque l'on ne dispose pas de suffisamment d'informations sur la conception détaillée et les exigences du projet. Les résultats ont révélé que le RàPC associé à \textit{bootstrap} est plus performant que le RàPC non associé à \textit{bootstrap}. + +Un modèle d'ensemble fondé sur le raisonnement à partir de cas est proposé par \cite{YU2023110163}. Celui-ci est appliqué à la prédiction financière et au remplissage des données manquantes. Dans ce système, pour retrouver les plus proches voisins, le modèle utilise trois mesures de distance différentes et une étape de vote pour l'intégration. Le modèle a été testé avec une base de données comportant onze caractéristiques (informations) financières provenant de 249 entreprises. La comparaison est faite avec deux objectifs. Premièrement, le remplissage des données manquantes avec d'autres algorithmes tels que KNN ou \textit{Random Forest}, et deuxièmement, la comparaison de la prédiction avec des algorithmes uniques utilisant une métrique de distance spécifique. Les résultats obtenus montrent que le système est plus performant aussi bien pour le remplissage des données manquantes que pour la prédiction des données financières. + +Une étape très importante dans le RàPC est l'adaptation. Dans \cite{10.1007/978-3-031-63646-2_4} l'objectif est changer la représentation de la connaissance pour évaluer s'il y a un impact positif en les solutions générées, ce changement permet aussi d'établir règles de adaptation, les résultats permettent de dire que choisir une représentation adéquate et des règles correctes en fonction du problème étudié peut améliorer la qualité des solutions pour résoudre des problèmes complexes. + +\section{Prédiction et interpolation en utilisant le RàPC} + +L'objectif du travail de \cite{10.1007/978-3-030-01081-2_25} est la prédiction du temps de course pour un athlète. L'algorithme KNN est utilisé pour rechercher les cas similaires. Le système interpole un temps final grâce au calcul d'une moyenne pondérée des meilleurs temps des cas similaires retrouvés dans la première étape du RàPC. + +Dans \cite{10.1007/978-3-030-58342-2_8}, les auteurs essaient de prédire le meilleur temps d'un patineur par analogie avec ceux des patineurs ayant des caractéristiques et une histoire de course similaires. Cependant parfois, calculer une moyenne des temps similaires trouvés ne suffit pas. Certaines caractéristiques liées au contexte, à l'environnement et à la nature de la course (le type de course, le type de piste, la distance à parcourir, etc.), peuvent en effet influencer de manière importante la performance du patineur. L'algorithme a été testé avec une base de données contenant les informations de 21 courses de 500m, 700m, 1000m, 1500m, 3km, 5km and 10km réalisées entre Septembre 2015 et Janvier 2020. + +Un système multi-fonctionnel est décrit dans \cite{10.1007/978-3-030-58342-2_5}. Celui-ci permet d'obtenir une prédiction du temps de course, de suggérer un plan du rythme de la course et il recommande également un plan d'entraînement pour une course donnée. Les trois fonctionnalités sont implémentées en utilisant le RàPC. Les calculs de la similarité sont fondés sur un historique et des caractéristiques physiques des coureurs. Les plans d'entraînement sont génériques et sont proposés sur les 16 semaines précédant le début du marathon ciblé (selon les auteurs, c'est en effet le temps usuel pour une préparation à ce type d'épreuve). Le système a été évalué avec une base de données constituée de caractéristiques de 21000 coureurs des marathons de Dublin, Londres ou New-York pour la période de 2014 à 2017. + +Les auteurs de \cite{YU2024123745} proposent un algorithme en deux étapes pour prédire avec précision et robustesse la détresse financière. Les deux étapes utilisent le RàPC. La première étape consiste à compléter les valeurs manquantes de la base de données considérée. Un cycle de RàPC est ensuite combiné avec un LVQ (\textit{Learning Vector Quantization}) pour interpoler une classification. Les résultats obtenus sont prometteurs. + +Les auteurs de \cite{Sadeghi} présentent un nouvel algorithme pour la prévision de la demande de matériel de secours. Le système décrit dans cet article combine le RàPC avec la méthode du meilleur-pire (\textit{Best-Worst Method}, BWM) et les modèles de Markov cachés (\textit{Hidden Markov Model}, HMM). Le HMM est entrainé avec des données historiques de demande de matériel. Lorsque l'algorithme détecte un accident, le RàPC recherche les cas similaires pour proposer le matériel nécessaire. D'après les résultats, l'indice d'erreur de prévision est inférieur à 10\%, démontrant ainsi la robustesse du système CBR-BWM-HMM proposé. + +\section{Recommandation, EIAH et RàPC} + +Les systèmes de recommandation et le RàPC peuvent aussi être combinés comme dans le système proposé par \cite{8495930}. Cet article montre les bénéfices de l'utilisation du RàPC dans les environnements informatiques pour l'apprentissage humain (EIAH). Le modèle proposé suit le cycle traditionnel du RàPC en combinant les modèles d'apprentissages traditionnels et numériques. Les principales contributions sont la représentation des cas et la recommandation des parcours d'apprentissage personnalisés selon les informations issues des autres apprenants. Une base de cas initiaux a été créée pour mesurer l'efficacité du modèle. Celle-ci stocke la recommandation du parcours de 120 apprenants. Des examens sont réalisés avant et après avoir suivi le parcours recommandé par le système permettent de mesurer l'efficacité de la recommandation proposée. + +Le système décrit dans \cite{Obeid} présente la particularité d'être capable d'analyser des données hétérogènes et multidimensionnelles. Dans ce travail, un parcours de carrière et les universités/collèges est recommandé aux élèves du secondaire en fonction de leurs intérêts. Ce travail montre également une taxonomie des techniques algorithmiques généralement utilisées dans les systèmes de recommandation pour les EIAH (figure \figref{figTax}). + +En fonction de certains paramètres comme le type d'école, le nombre d'étudiants admis et non admis, le nombre de classes, les auteurs de \cite{skittou2024recommender} développent un modèle hybride combinant RàPC et raisonnement à partir de règles pour recommander des actions de planification en réponse à des cas éducatifs des écoles primaires. Le modèle a été testé sur des données réelles et a donné de bons résultats avec une grande précision. + +%Le RàPC a également été mis en oeuvre dans différents travaux liés à la génération, l’analyse et la correction de textes écrits. Pour la réalisation de ces tâches, il est parfois nécessaire de transformer le texte en représentation numérique ou d’établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit dans ce travail explore les transformations de forme possibles afin que la nouvelle histoire ne soit pas similaire aux histoires déjà connues tout en étant cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si l’histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit être recommencé. L'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en \colorbox{pink}{unifiant tout avec des actions ??}. Les résultats montrent que la plupart des histoires générées sont cohérentes mais qu'elles sont constituées de paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20}, les phrases écrites en langue française sont corrigées, ce travail n'utilise ni la transformation numérique \colorbox{yellow}{?} des phrases, ni des connaissances linguistiques. Le système conçu retrouve des phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant la distance entre toutes les phrases \colorbox{yellow}{lesquelles ?} pour savoir \colorbox{pink}{je ne comprends pas la suite }si elle est bien écrite ou pas. Si la phrase n’est pas bien écrite, le système peut proposer une correction en changeant certains mots selon le contexte et calculer à nouveau les distances mesurant la cohérence et pertinence de la phrase. + +\mfigure[!ht]{width=\textwidth}{./Figures/taxonomieEIAH.png}{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (Traduit de \cite{Obeid})}{figTax} + +%\begin{figure} +%\centering +%\includegraphics[scale=21]{./Figures/taxonomieEIAH.png} +%\caption{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (\cite{Obeid})} +%\label{figTax} +%\end{figure} + +\section{Récapitulatif des limites des travaux présentés dans ce chapitre} + +Le tableau \ref{tabArts2} récapitule les articles analysés dans cet état de l'art du RàPC et rappelle les limitations que nous avons identifiées. Ces limitations sont liées aux données, à la flexibilité, à la généralisation de l'algorithme proposé, à la validité des solutions proposées, l'automatisation du processus et complexité du modèle proposée. + +En complément, deux problèmes très communs des systèmes de recommandation peuvent être ajoutées à ces limitations : +\begin{itemize} + \item le \textit{cold-start} qui se produit au début d'une recommandation lorsqu'il n'y a pas suffisamment de données pour calculer ou inférer une recommandation appropriée \cite{HU2025127130}, et + \item le \textit{gray-sheep} qui se produit lorsqu'un utilisateur présente un comportement très différent de ceux qui sont stockés dans la base de données. Le système ne peut donc pas générer des recommandations en se basant sur l'information disponible \cite{ALABDULRAHMAN2021114061}. +\end{itemize} + +\begin{table} +\footnotesize +\begin{tabular}{p{4.4cm}p{9cm}} +Ref&Limites\\ +\hline + +\cite{JUNG20095695}&Le modèle d'adaptation proposé fonctionne seulement avec des cas très proches. L'apprentissage dans le RàCP se limite à stocker les nouveaux cas.\\ + +\cite{10.1007/978-3-642-15973-2_50}&Le RàPC n'est pas modifié ou amélioré. La révision dans le RàPC n'est pas automatique\\ + +\cite{PETROVIC201617}&Grande quantité de données pour que l'algorithme fonctionne correctement. Recommandation limitée à un problème très spécifique.\\ + +\cite{wolf2024keep}&Le modèle n'est actuellement appliqué qu'à la classification d'images à un seul label \\ + +\cite{PAREJASLLANOVARCED2024111469}&La base de données de test a été construite avec des évaluations subjectives. Le modèle ne considère pas les incertitudes.\\ + +\cite{ROLDANREYES20151}&Une seule méthode d'adaptation est utilisée. Le modèle n'exploite pas les cas qui présentent une erreur.\\ + +\cite{10.1007/978-3-319-47096-2_11}&Beaucoup de données sont nécessaires pour entrainer le modèle de 'Deep Learning'. Les solutions générées n'ont pas été validées.\\ + +\cite{10.1007/978-3-319-61030-6_1}&L'évaluation des solutions générées n'est pas automatique. Les solutions sont produites avec un seul point de vue.\\ + +\cite{buildings13030651}&La fonction d'unification des solutions est une moyenne des solutions générées avec la division des données et un seul approche\\ + +\cite{YU2023110163}&Une base de données déséquilibrée peut affecter négativement la performance du modèle proposé\\ + +\cite{Muller}&Une seule approche pour adapter les cas dans le RàPC. La révision dans le RàPC n'est pas automatique.\\ + +\cite{10.1007/978-3-319-24586-7_20}&Les solutions générées peuvent présenter des incohérences. Il n'y a pas une métrique objective pour mesurer la qualité des réponses.\\ + +\cite{10.1007/978-3-030-58342-2_20}&Les résultats ne sont pas très bons. Le modèle n'a pas de connaissances linguistiques.\\ + +\cite{10.1007/978-3-031-63646-2_4}&Les règles d'adaptation définies sont fixes et peuvent être limitées pour certaines situations\\ + +\cite{10.1007/978-3-030-01081-2_25}&Les prédictions n'ont pas été testées dans le monde réel. Les données sont très spécifiques et peu variées.\\ + +\cite{10.1007/978-3-030-58342-2_8}&Les prédictions sont pour des cas limités. Le modèle est entrainé pour un type d'utilisateur spécifique.\\ + +\cite{10.1007/978-3-030-58342-2_5}&Seulement une technique de sélection de solutions. Les données nécessaires pour le modèle sont complexes.\\ + +\cite{YU2024123745}&Il n'y a pas de révision des cas dans la méthode d'imputation ni dans le modèle de classification fondé sur RàPC. Cela peut accroître le biais d'imputation et introduire des échantillons avec bruit\\ + +\cite{Sadeghi}&Le temps de calcul peut être très grand en fonction de la quantité de données associées, du a l'étape d'entrainement du modèle HMM\\ + +\cite{8495930}&Le modèle a été validé avec peu de données. Les recommandations se basent seulement sur l'information des autres apprenants.\\ + +\cite{Obeid}&L'ontologie peut être limitée pour évaluer plusieurs cas inconnus ou imprévus. Il est nécessaire d'avoir une forte connaissance du domaine.\\ + +\cite{skittou2024recommender}&Les règles de décision établies sont fixes et avec l'hybridation peut avoir de l'incertitude et l'imprécision\\ + +\end{tabular} +\caption{Tableau de synthèse des articles analysés dans l’état de l’art du RàPC} +\label{tabArts2} +\end{table} + +%\begin{figure} +%\centering +%\includegraphics[scale=0.4]{./Figures/ModCBR2.png} +%\caption{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (\cite{10.1007/978-3-319-47096-2_11})} +%\label{figMCBR2} +%\end{figure} diff --git a/chapters/CBR0.tex b/chapters/CBR0.tex new file mode 100644 index 0000000..5bec0c6 --- /dev/null +++ b/chapters/CBR0.tex @@ -0,0 +1,135 @@ +\chapter{\'Etat de l'art (Raisonnement à Partir de Cas)} + +\section{Raisonnement à partir de cas (RàPC)} + +Le raisonnement à partir de cas est une approche fondée sur la connaissance. Il s'agit d'une technique d'intelligence artificielle dont l'idée est de résoudre un nouveau problème grâce aux connaissances déjà acquises par le système et en tenant un raisonnement fondé sur l'analogie. Le RàPC est apparu comme une alternative pour améliorer les systèmes experts. Shank et Abelson \cite{schank+abelson77} ont initialement mené des travaux sur l'organisation hiérarchique de la mémoire pour imiter le raisonnement humain. Ceux-ci ont servi de fondement aux travaux de Janet Kolodner \cite{KOLODNER1983281} et ont abouti à l'implémentation un système fondé sur ces principes en 1983. Le terme \textit{raisonnement à partir de cas} est utilisé pour la première fois en 1989 par Riesbeck et Shank \cite{Riesbeck1989}. + +Comme vu dans le chapitre du contexte, le raisonnement à partir de cas utilise quatre conteneurs de connaissance pour représenter la connaissance complète du système, chaque conteneur stocke l'information associée à une fonction spécifique et tout est fondé sur le carré d'analogie, c'est-à-dire des solutions ayant permis de résoudre un problème ancien sont réutilisées afin de résoudre un problème nouveau similaire. La plupart des systèmes de RàPC utilisent pour son fonctionnement comme base un cycle composé de quatre étapes (retrouver, réutiliser, réviser et retenir). + +Les travaux ici cités sont des travaux représentatifs parce que ils ont permis de trouver certaines points d'amélioration du RàPC, ont donné des pistes d'intégration avec d'autres techniques ou ont donné des idées de modification pour obtenir bons résultats en gagnant de la performance. + +Plusieurs travaux combinent le RàPC avec les réseaux de neurones (Deep Learning) avec l'objectif d'améliorer les réponses générées et optimiser les performances de chaque algorithme. C'est une idée qui marche dans certains cas, mais qui n'est pas très récente comme par exemple dans \cite{JUNG20095695} les auteurs développent un système fondé sur l'hybridation du RàPC avec des réseaux de neurones pour concevoir des produits. Le système se focalise uniquement dans les phases "rechercher" et "réutiliser" du RàPC dans lesquelles sont exécutés les algorithmes implémentés. Le système détermine de façon automatique les valeurs pour les paramètres nécessaires à la conception d'un produit particulier en suivant le cycle traditionnel du RàPC. Avec l'algorithme de k-moyennes, est extrait un cas représentatif de la base de cas et l'adaptation des solutions des voisins trouvées est faite avec le réseau de neurones RBFN (Radial Basis Function Network). L'évaluation du système est faite en utilisant une base de données contenant 830 tests, les résultats démontrent que les produits conçus s'ajustent avec un grand pourcentage aux normes définies. + +Dans \cite{10.1007/978-3-642-15973-2_50} un réseau de neurones classique est implémenté pour définir la géométrie d'une matrice pour l'extrusion de l'aluminium. En effet, actuellement c'est un processus qui se fait manuellement et par essai et erreur. Le RàPC est alors utilisé pour aider à déterminer les valeurs optimales des paramètres du réseau, en utilisant l'information des matrices d'extrusion déjà testées. + +Aussi le travail de \cite{PETROVIC201617} où les auteurs proposent un système de raisonnement à partir de cas pour calculer la dose optimale de radiation pour le traitement du cancer. Dans ce domaine particulier de la radiothérapie, administrer une dose nécessite de connaître avec précision le nombre de faisceaux et l'angle de chacun d'eux. L'algorithme proposé tente de trouver la combinaison de valeurs optimales pour ces deux paramètres en utilisant les réseaux de neurones. L'utilisation des réseaux de neurones intervient lors de l'adaptation des cas connus : ils modifient le nombre et les angles des faisceaux. La validation de l'algorithme est évaluée avec une base de 80 cas réels de cancer du cerveau extraits de l'hôpital de Nottingham City. Le nombre de neurones et de couches ont été définis de façon empirique. Les résultats montrent que l'utilisation des cas historiques et la construction des solutions à partir des solutions déjà connues permet une amélioration de 12\% concernant la décision du nombre de faisceaux et de 29\% concernant la décision liée à leur angle. + +Plus récemment se trouvent travaux comme \cite{wolf2024keep} où le RàPC est employée comme technique pour expliquer les résultats générés par un réseau qui classifie les images en fonction de certains attributs et zones dans les images, le résultats permettent conclure que les explications trouvées avec RàPC sont très fidèles aux images testées. Aussi \cite{PAREJASLLANOVARCED2024111469} utilisent le RàPC et ses avantages pour le coupler à un réseau profond (Deep Learing) pour sélectionner la meilleure explication au résultat donné par le classificateur d'images dans ce cas, la base de connaissance sont certaines images avec des étiquettes qui contiennent l'information associée à l'image, la comparaison réalisée avec les algorithmes représentatifs de l'état de l'art suggèrent que la combinaison Deep Learning marche bien dans ce type de problèmes. + +Certains travaux ont appliqué le raisonnement à partir de cas à un problème spécifique en modifiant les représentations des cas et des solutions, d'autres ont modifié le cycle conceptuel comme le montre la figure \figref{figMCBR2} avec l'objectif d'obtenir des réponses dynamiques et plus aptes à problèmes complexes. Dans \cite{10.1007/978-3-319-47096-2_11} est proposé un cycle complémentaire incluant un outil d'apprentissage profond (\textit{Deep Learning}) pour améliorer le résultat du processus du RàPC. Dans \cite{10.1007/978-3-642-15973-2_50}, la phase de stockage est modifiée en retenant les cas dont les résultats n'ont pas eu de succès, pour guider le processus dans la fabrications de nouvelles pièces. Enfin, dans \cite{Robertson2014ARO}, les auteurs proposent d'ajouter à chaque cas une valeur d'utilité espérée selon chaque action possible. Cet ajout s'accompagne d'une prédiction probabiliste des actions que l'application engendrera en réponse. Cette prédiction probabiliste dépend bien entendu de l'état initial du système avant mise en oeuvre de l'action. + +\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR2.png}{Ajout d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (Traduit de \cite{10.1007/978-3-319-47096-2_11})}{figMCBR2} + +Plusieurs travaux appliquent le RàPC avec succès en proposant des modifications dans chacune des phases ou en combinant différents algorithmes. Certains systèmes de RàPC appliqués au domaine de la conception de produits sont remarquables à ce titre. Dans \cite{ROLDANREYES20151} les auteurs proposent, comme le montre la figure \figref{figMCBR1}, un algorithme pour produire le propylène glycol dans un réacteur chimique. Dans ce cas, la phase de réutilisation du RàPC est couplée à la recherche des états qui satisfassent le nouveau problème (\textit{Constraint satisfaction problems CSP}) en utilisant l'information des cas de base déjà résolus. Les solutions trouvées sont évaluées selon le nombre de changements réalisés sur les solutions déjà connues (parcimonie), le nombre de solutions possibles trouvées (précision), l'évaluation de commentaires faits par des experts et la complexité des transformations réalisées. + +\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR1.png}{Cycle du RàPC modifié. (Traduit de \cite{ROLDANREYES20151})}{figMCBR1} + +Dans \cite{10.1007/978-3-319-47096-2_11}, les auteurs ajoutent un nouveau cycle au cycle traditionnel du RàPC. Le premier cycle génère des descriptions abstraites du problème avec un réseau de neurones et des algorithmes génétiques; le second cycle prend les descriptions abstraites comme des nouveaux cas, cherche les cas similaires et adapte les solutions rencontrées. L'exécution des deux cycles prend en compte certains critères prédéfinis par l'utilisateur. En comparant le même problème avec le cycle traditionnel du RàPC, les auteurs mesurent une amélioration de la qualité des recettes proposées et montrent que celles-ci sont plus en accord avec les critères définis. + +\cite{10.1007/978-3-319-61030-6_1} s'intéressent quant à eux à la génération de recettes de cuisine originales. Les auteurs modifient le cycle traditionnel du RàPC et créent deux cycles, combinant l'apprentissage profond et la récupération de cas basée sur la similarité, le premier cycle génère des descriptions abstraites de la conception avec des algorithmes génétiques et un réseau neuronal profond, le second cycle utilise les descriptions abstraites pour récupérer et adapter des objets, cette structure donne lieu à un prototype appelé Q-chef qui génère une recette basée sur la base de données d'ingrédients et les demandes de l'utilisateur. Ce travail ne montre pas de métriques standard génériques mais utilise deux nombres indicatifs (plausibilité et surprise) pour démontrer la génération efficace de nouvelles recettes selon les critères de l'utilisateur en comparant le RàPC à deux cycles avec le RàPC à un cycle, démontrant plus de plausibilité et de surprise dans les recettes générées. + +Par rapport à l'intégration du RàPC avec d'autres algorithmes nous pouvons citer \cite{buildings13030651} qui mettent en Å“uvre un RáPC avec une méthode d'agrégation bootstrap (bagging) pour améliorer la précision du RàPC lorsque il n'y a pas suffisamment de cas et réduire la variance. Le problème qui ils essaient de résoudre est l'estimation des coûts conceptuels dans les décisions de faisabilité d'un projet, lorsque l'on ne dispose pas de suffisamment d'informations sur la conception détaillée et les exigences du projet. Les résultats ont révélé que la performance de prédiction de la méthode RàPC agrégée bootstrap est meilleure que la performance de prédiction de la méthode RàPC traditionnelle. + +Un modèle d'ensemble fondé sur le raisonnement à partir de cas est proposé par \cite{YU2023110163} appliqué à la prédiction financière et au remplissage des données manquantes. Dans ce cas, pour retrouver les plus proches voisins, le modèle utilise trois mesures de distance différentes et une étape de vote pour l'intégration. Le modèle a été testé avec une base de données comportant onze dimensions d'informations financières provenant de 249 entreprises. La comparaison est faite avec deux objectifs. Premièrement, le remplissage des données manquantes avec d'autres algorithmes tels que KNN ou RandomForest, et deuxièmement, la comparaison de la prédiction avec des algorithmes uniques utilisant une métrique de distance spécifique. En effet, les résultats montrent une meilleure performance dans le remplissage des données manquantes et les meilleurs résultats dans la prédiction. + +La représentation des cas peut permettre également d'améliorer les résultats d'un système de RàPC. La performance d'un système de RàPC dépend de la quantité d'informations stockées, mais également des algorithmes implémentés. C'est le cas dans \cite{Muller} où les recettes de cuisine sont codées comme un processus de transformation et mélangent des ingrédients en suivant une suite d'étapes ordonnés. Pour créer des recettes innovantes, une mesure de distance est utilisée entre les ingrédients. Cette mesure permet de trouver une recette en substituant certains ingrédients par d'autres, similaires ou aux qualités et/ou caractéristiques similaires. De la même manière, il est possible de créer des recettes plus proches des exigences des utilisateurs. Les étapes de transformation appelées aussi opérateurs sont stockées et catégorisées grâce à une métrique permettant de les échanger afin d'obtenir une nouvelle recette. + +La génération, analyse et correction de texte constitue également un domaine d'application intéressant du RàPC. Pour la réalisation de ces tâches il est parfois nécessaire de transformer le texte en représentation numérique ou de mesurer la proximité sémantique de mots. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit explore les transformations possibles afin que la nouvelle histoire ne soit pas très similaire aux histoires déjà connues mais que elle soit cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de celle-ci déterminent si l'histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit recommencer; l'adaptation se fait en recherchant les personnages, les contextes, les objets dans la base de cas, et en les unifiant avec des actions; la plupart des histoires générées sont cohérentes mais elles sont constituées de paragraphes très courts. + +Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées. Ce travail n'utilise ni la transformation numérique des phrases, ni de connaissances linguistiques, mais retrouve les phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant une mesure de distance avec les phrases correctes et incorrectes de la base de connaissances. Si les phrases similaires sont incorrectes, le système peut proposer une correction en changeant certains mots selon le contexte et recalculer les distances afin de mesurer la cohérence et pertinence de la phrase proposée. + +Une étape très importante dans le RàPC est l'adaptation. Dans \cite{10.1007/978-3-031-63646-2_4} l'objectif est changer la représentation de la connaissance pour évaluer s'il y a un impact positif en les solutions générées, ce changement permet aussi d'établir règles de adaptation, les résultats permettent de dire que choisir une représentation adéquate et des règles correctes en fonction du problème étudié peut améliorer la qualité des solutions pour résoudre des problèmes complexes. + +Étant donne la versatilité du RàPC dans la littérature se trouvent des travaux qui l'utilisent comme un outil de prédiction, car en connaissant l'information historique généralement est possible d'inférer la structure ou le comportement des données et avec le RàPC les résultats sont assez bons. + +L'objectif du travail de \cite{10.1007/978-3-030-01081-2_25} est la prédiction du temps de course pour un athlète. L'algorithme KNN est utilisé pour rechercher les cas similaires. Le système interpole un temps final grâce au calcul d'une moyenne pondérée des meilleurs temps des cas similaires retrouvés dans la première étape du RàPC. + +Dans \cite{10.1007/978-3-030-58342-2_8}, les auteurs essaient de prédire le meilleur temps d'un patineur par analogie avec ceux des patineurs ayant des caractéristiques et une histoire de course similaires. Cependant parfois, calculer une moyenne des temps similaires trouvés ne suffit pas. Certaines caractéristiques liées au contexte, à l'environnement et à la nature de la course (le type de course, le type de piste, la distance à parcourir, etc.), peuvent en effet influencer de manière importante la performance du patineur. L'algorithme a été testé avec une base de données contenant les informations de 21 courses de 500m, 700m, 1000m, 1500m, 3km, 5km and 10km réalisées entre Septembre 2015 et Janvier 2020. + +Un système multi-fonctionnel est décrit dans \cite{10.1007/978-3-030-58342-2_5}. Celui-ci permet d'obtenir une prédiction du temps de course, de suggérer un plan du rythme de la course et il recommande également un plan d'entraînement pour une course donnée. Les trois fonctionnalités sont implémentées en utilisant le RàPC. Les calculs de la similarité sont fondés sur un historique et des caractéristiques physiques des coureurs. Les plans d'entraînement sont génériques et sont proposés sur les 16 semaines précédant le début du marathon ciblé (selon les auteurs, c'est en effet le temps usuel pour une préparation à ce type d'épreuve). Le système a été évalué avec une base de données constituée de caractéristiques de 21000 coureurs des marathons de Dublin, Londres ou New-York pour la période de 2014 à 2017. + +Dans \cite{YU2024123745} est proposé un algorithme en deux stages pour prédire avec précision et robustesse la détresse financière, les deux stages utilisent le RàPC. Le premier stage permet de compléter les valeurs manquantes de la base de données choisie afin de obtenir une meilleure performance fiable et stable, après est exécutée une version du RàPC combinée avec un LVQ (Learning Vector Quantization) algorithme pour faire la prédiction de classification. Les résultats démontrent compétitive solutions pour compléter les valeurs manquantes et classification. + +\cite{Sadeghi} présentent un nouvel algorithme pour la prévision de la demande de matériel de secours utilisant le RàPC avec la méthode du meilleur-pire (Best-Worst Method - BWM) et les modèles de Markov cachés (Hidden Markov Model - HMM). Le HMM est entrainé avec des données historiques de demande de matériel, une fois l'algorithme détecte une situation de catastrophe, le RàPC cherche les cas similaires pour proposer le matériel nécessaire. D'après les résultats, l'indice d'erreur de prévision est inférieur à 10\%, par conséquent, le CBR-BWM-HMM proposé est un algorithme solide et robuste. + +Les systèmes de recommandation et le RàPC peuvent aussi être combinés comme dans le système proposé par \cite{8495930} montre les bénéfices de l'utilisation du RàPC dans les environnements informatiques pour l'apprentissage humain (EIAH). Le modèle proposé suit le cycle traditionnel du RàPC en combinant les modèles d'apprentissages traditionnels et numériques. Les principales contributions sont la représentation des cas et la recommandation des parcours d'apprentissage personnalisés selon les informations issues des autres apprenants. Une base de cas initiaux a été créée pour mesurer l'efficacité du modèle. Celle-ci stocke la recommandation du parcours de 120 apprenants. Des examens sont réalisés avant et après avoir suivi le parcours recommandé par le système permettent de mesurer l'efficacité de la recommandation proposée. + +\cite{Obeid}. Le système décrit dans cette étude présente la particularité d'être capable d'analyser des données hétérogènes et multidimensionnelles. Dans ce travail, un parcours de carrière et les universités/collèges est recommandé aux élèves du secondaire en fonction de leurs intérêts. Ce travail montre également une taxonomie des techniques algorithmiques généralement utilisées dans les systèmes de recommandation pour les EIAH (figure \figref{figTax}). + +En fonction de certains paramètres comme type d'école, nombre d'étudiants, nombre d'étudiants admis, nombre d'étudiants non-admis, total nombre de classes, etc. +\cite{skittou2024recommender} développent un modèle hybride entre RàPC et le raisonnement à partir de règles pour recommander des actions de planification de l'éducation en réponse à des cas éducatifs des écoles primaires. Le modèle a été testé des bases de données réelles et a donné de bons résultats avec une grande précision. + +%Le RàPC a également été mis en oeuvre dans différents travaux liés à la génération, l’analyse et la correction de textes écrits. Pour la réalisation de ces tâches, il est parfois nécessaire de transformer le texte en représentation numérique ou d’établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système décrit dans ce travail explore les transformations de forme possibles afin que la nouvelle histoire ne soit pas similaire aux histoires déjà connues tout en étant cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si l’histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit être recommencé. L'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en \colorbox{pink}{unifiant tout avec des actions ??}. Les résultats montrent que la plupart des histoires générées sont cohérentes mais qu'elles sont constituées de paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20}, les phrases écrites en langue française sont corrigées, ce travail n'utilise ni la transformation numérique \colorbox{yellow}{?} des phrases, ni des connaissances linguistiques. Le système conçu retrouve des phrases similaires en utilisant l'algorithme LCS (\textit{Longest Common Subsequence}) et en calculant la distance entre toutes les phrases \colorbox{yellow}{lesquelles ?} pour savoir \colorbox{pink}{je ne comprends pas la suite }si elle est bien écrite ou pas. Si la phrase n’est pas bien écrite, le système peut proposer une correction en changeant certains mots selon le contexte et calculer à nouveau les distances mesurant la cohérence et pertinence de la phrase. + +\mfigure[!ht]{width=\textwidth}{./Figures/taxonomieEIAH.png}{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (Traduit de \cite{Obeid})}{figTax} + +%\begin{figure} +%\centering +%\includegraphics[scale=21]{./Figures/taxonomieEIAH.png} +%\caption{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (\cite{Obeid})} +%\label{figTax} +%\end{figure} + +Le tableau \ref{tabArts2} montre un récapitulatif des articles analysés dans cet état de l'art du RàPC, où est chacun d'eux en ordre d'apparition dans le chapitre et les limitations trouvées par rapport aux données, flexibilité, généralité de l'algorithme proposé, validité des solutions proposées, automatisation du processus et complexité du modèle proposée. À part les limites identifiés dans le tableau, nous avons trouvé deux problèmes très communs des systèmes de recommandation de façon générale : (1) le \textit{cold-start} qui se produit au début d'une recommandation lorsque il n'y a pas suffisamment de données pour calculer ou inférer une recommandation appropriée \cite{HU2025127130}. Et (2) le \textit{gray-sheep} qui se produit lorsqu'un utilisateur présente un comportement très différent de ceux qui sont stockés dans la base de données. Le système ne peut donc pas générer des recommandations en se basant sur l'information disponible \cite{ALABDULRAHMAN2021114061}. + +\begin{table} +\footnotesize +\begin{tabular}{p{4.4cm}p{9cm}} +Ref&Limites\\ +\hline + +\cite{JUNG20095695}&Le modèle d'adaptation proposé fonctionne seulement avec des cas très proches. L'apprentissage dans le RàCP se limite à stocker les nouveaux cas.\\ + +\cite{10.1007/978-3-642-15973-2_50}&Le RàPC n'est pas modifié ou amélioré. La révision dans le RàPC n'est pas automatique\\ + +\cite{PETROVIC201617}&Grande quantité de données pour que l'algorithme fonctionne correctement. Recommandation limitée à un problème très spécifique.\\ + +\cite{wolf2024keep}&Le modèle n'est actuellement appliqué qu'à la classification d'images à un seul label \\ + +\cite{PAREJASLLANOVARCED2024111469}&La base de données de test a été construite avec des évaluations subjectives. Le modèle ne considère pas les incertitudes.\\ + +\cite{ROLDANREYES20151}&Une seule méthode d'adaptation est utilisée. Le modèle n'exploite pas les cas qui présentent une erreur.\\ + +\cite{10.1007/978-3-319-47096-2_11}&Beaucoup de données sont nécessaires pour entrainer le modèle de 'Deep Learning'. Les solutions générées n'ont pas été validées.\\ + +\cite{10.1007/978-3-319-61030-6_1}&L'évaluation des solutions générées n'est pas automatique. Les solutions sont produites avec un seul point de vue.\\ + +\cite{buildings13030651}&La fonction d'unification des solutions est une moyenne des solutions générées avec la division des données et un seul approche\\ + +\cite{YU2023110163}&Une base de données déséquilibrée peut affecter négativement la performance du modèle proposé\\ + +\cite{Muller}&Une seule approche pour adapter les cas dans le RàPC. La révision dans le RàPC n'est pas automatique.\\ + +\cite{10.1007/978-3-319-24586-7_20}&Les solutions générées peuvent présenter des incohérences. Il n'y a pas une métrique objective pour mesurer la qualité des réponses.\\ + +\cite{10.1007/978-3-030-58342-2_20}&Les résultats ne sont pas très bons. Le modèle n'a pas de connaissances linguistiques.\\ + +\cite{10.1007/978-3-031-63646-2_4}&Les règles d'adaptation définies sont fixes et peuvent être limitées pour certaines situations\\ + +\cite{10.1007/978-3-030-01081-2_25}&Les prédictions n'ont pas été testées dans le monde réel. Les données sont très spécifiques et peu variées.\\ + +\cite{10.1007/978-3-030-58342-2_8}&Les prédictions sont pour des cas limités. Le modèle est entrainé pour un type d'utilisateur spécifique.\\ + +\cite{10.1007/978-3-030-58342-2_5}&Seulement une technique de sélection de solutions. Les données nécessaires pour le modèle sont complexes.\\ + +\cite{YU2024123745}&Il n'y a pas de révision des cas dans la méthode d'imputation ni dans le modèle de classification fondé sur RàPC. Cela peut accroître le biais d'imputation et introduire des échantillons avec bruit\\ + +\cite{Sadeghi}&Le temps de calcul peut être très grand en fonction de la quantité de données associées, du a l'étape d'entrainement du modèle HMM\\ + +\cite{8495930}&Le modèle a été validé avec peu de données. Les recommandations se basent seulement sur l'information des autres apprenants.\\ + +\cite{Obeid}&L'ontologie peut être limitée pour évaluer plusieurs cas inconnus ou imprévus. Il est nécessaire d'avoir une forte connaissance du domaine.\\ + +\cite{skittou2024recommender}&Les règles de décision établies sont fixes et avec l'hybridation peut avoir de l'incertitude et l'imprécision\\ + +\end{tabular} +\caption{Tableau de synthèse des articles analysés dans l’état de l’art du RàPC} +\label{tabArts2} +\end{table} + +%\begin{figure} +%\centering +%\includegraphics[scale=0.4]{./Figures/ModCBR2.png} +%\caption{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (\cite{10.1007/978-3-319-47096-2_11})} +%\label{figMCBR2} +%\end{figure} diff --git a/chapters/CBR_0.tex b/chapters/CBR_0.tex deleted file mode 100644 index 4bd49c6..0000000 --- a/chapters/CBR_0.tex +++ /dev/null @@ -1,89 +0,0 @@ -\chapter{\'Etat de l'art (Raisonnement à Partir de Cas)} - -\section{Raisonnement à partir de cas (RàPC)} - -Le raisonnement à partir de cas est une approche fondé sur la connaissance, est une technique d'intelligence artificielle dont l'idée est de résoudre un nouveau problème donné en utilisant l'analogie avec les connaissances déjà acquises par le système. Le RàPC est apparue comme une alternative pour améliorer les systèmes experts. Initialement en 1977 les travaux de Shank et Abelson \cite{schank+abelson77} sur l'organisation hiérarchique de la mémoire pour imiter le raisonnement humain ont permis d'implémenter un système a Kolodner \cite{KOLODNER1983281} en l'année 1983 pour arriver au terme RàPC en 1989 dans le travail de Riesbeck et Shank \cite{Riesbeck1989}. - -Le raisonnement à partir de cas a été utilisé dans différents domaines comme : médecine, conception de produits, sports, systèmes de recommandation, etc. En médecine a été appliqué pour explorer et aider la prise de décisions des médecins dans les traitements qui requièrent administration de doses précises selon les cas et les patients, \cite{PETROVIC201617} propose un algorithme de raisonnement à partir de cas qui indique quel est la dose correcte de radiation pour le traitement du cancer. Pour administrer une dose de radiothérapie il est nécessaire de connaître avec précision deux paramètres: le nombre de faisceaux et l'angle de chacun d'eux. L'algorithme proposé essaie de trouver la valeur optimal pour la combinaison des deux paramètres en utilisant les réseaux de neurones et l'adaptation des cas connus pour changer le nombre de faisceaux et adapter le angle de chaque faisceau. La validation de l'algorithme est évaluée avec une base de 80 cas réels de cancer du cerveau extraits de l'hôpital de Nottingham City, le nombre de neurones et couches a été changé de façon empirique. Les résultats montrent que l'utilisation des cas historiques et la construction des solutions à partir des solutions déjà connues atteint une amélioration de 12\% dans les cas du nombre de faisceaux et 29\% dans les cas de l'angle des faisceaux. - -Plusieurs travaux ont obtenu de bons résultats en appliquant le RàPC avec des modifications dans chacune des phases ou en combinant différents algorithmes au design de produits. \cite{ROLDANREYES20151} propose comme le montre la figure \figref{figMCBR1} un algorithme pour produire propylène glycol dans un réacteur chimique, dans ce cas la phase de réutilisation du RàPC est mélangée avec la recherche des états qui satisfassent le nouveau problème (Constraint satisfaction problems CSP) en utilisant l'information des cas de base déjà résolus. Les solutions trouvées sont évaluées selon le nombre de changements faits aux solutions déjà connues (Parcimonie), le nombre de possibles solutions trouvées (Accuracy), l'évaluation de commentaires faits par des experts et la complexité des transformations réalisées. Il n'est pas obligatoire de modifier tout le cycle du RàPC pour l'adapter à une problématique spécifique, comme \cite{JUNG20095695} qui développe un système basé sur l'hybridation du RàPC avec des réseaux de neurones pour concevoir des produits, le système se focalise uniquement dans les phases de rechercher et réutiliser du RàPC où sont exécutes les algorithmes implémentés. Le système détermine de façon automatique les valeurs pour les paramètres nécessaires à la conception de un produit particulier en suivant le cycle traditionnel du RàPC. Avec l'algorithme de k-moyennes est extrait un cas représentatif de la base de cas et l'adaptation des solutions des voisins trouvées est faite avec le réseau de neurones RBFN. Avec 830 test, les nouveaux cas très similaires aux cas de la base initiale est obtenu un bon design en 99.4\% des cas, pour les cas dissemblables le pourcentage est de 94.8\%. Le RàPC peut être utilisé aussi comme outil secondaire pour aider d'autres algorithmes à trouver la bonne configuration, dans \cite{10.1007/978-3-642-15973-2_50} est implémenté un réseau de neurones classique pour designer la géométrie d'une matrice pour l'extrusion de l'aluminium, car actuellement c'est un processus qui se fait manuellement par essai et erreur. Le RàPC ici est utilisé pour aider à déterminer les valeurs optimales des paramètres du réseau. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR1.png}{Cycle du RàPC modifié. (Traduit de \cite{ROLDANREYES20151})}{figMCBR1} - -%\begin{figure} -%\centering -%\includegraphics[scale=0.4]{./Figures/ModCBR1.png} -%\caption{Cycle du RàPC modifié. (\cite{ROLDANREYES20151})} -%\label{figMCBR1} -%\end{figure} - -L'une des problématiques la plus représentative et la plus fondamentale de l'utilisation du RàPC est la création de recettes de cuisine à partir de une base de ingrédients et d'un ensemble de recettes déjà connues, mais malgré l'apparente simplicité du problème il y a beaucoup de variations et modèles, dans \cite{10.1007/978-3-319-47096-2_11} est abordée cette problématique, mais en additionnant un nouveau cycle au cycle traditionnel du RàPC. Le premier cycle génère des descriptions abstraites du problème avec un réseau de neurones et algorithmes génétiques; le seconde cycle prend les descriptions abstraites comme nouveaux cas, cherche les cas similaires et adapte les solutions rencontrées. L'exécution des deux cycles considère certains critères prédéfinis par l'utilisateur. En comparant le même problème avec le cycle traditionnel du RàPC il y a effectivement une amélioration de la qualité des recettes proposées et qui vont plus en accord avec les critères définis. Sur la même problématique, mais plus focalisé sur la génération des recettes novatrices, \cite{10.1007/978-3-319-61030-6_1}, utilise un modèle probabiliste qui considère les préférences des utilisateurs, un indicateur de curiosité et un composant qui synthétise tout le design; une fois que toutes les exigences sont remplies, la recette est créée en combinant les ingrédients avec un réseau d'apprentissage profond. Les changements dans la représentation des cas est aussi une source d'amélioration des résultats parce que parfois d'un côté il y a plus d'information et de l'autre certain techniques ou algorithmes fonctionnent mieux, c'est le cas que montre \cite{Muller} où les recettes sont codées comme un processus de transformation et mélange des ingrédients en suivant une suite de pas ordonnés, pour créer innovation dans les recettes une mesure de distance est employée entre les ingrédients avec laquelle il est possible de trouver une recette et changer les ingrédients pour les plus proches, de la même façon il est possible de créer recettes plus proches des exigences des utilisateurs, les étapes de transformation appelées aussi opérateurs sont stockes et catégorisées les unes avec les autres avec une métrique qui permet de les échanger pour obtenir une nouvelle recette. - -Une autre type d'application du RàPC très notable est le génération, analyse et correction du texte écrit; pour la réalisation de ces tâches parfois il est nécessaire de transformer le texte en représentation numérique ou établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires, en explorant les possibles transformations de forme que la nouvelle histoire ne soit pas très similaire aux histoire déjà connues mais que elle soit cohérent. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si la histoire générée corresponde avec les critères spécifiés ou si le cycle d'adaptation doit recommencer; l'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en unifiant tout avec des actions; la plus part des histoires générées sont cohérents mais sont des paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées, ce travail n'utilise pas la transformation numérique des phrases, ni connaissances linguistiques, mais seulement retrouve les phrases similaires en utilisant l'algorithme LCS (Longest Common Subsequence) et en calculant la distance parmi toutes les phrases pour savoir si elle est bien écrite ou pas, sinon le système peut proposer une correction en changeant certains mots selon le contexte et calculer à nouveau les distances qui déterminent la cohérence et pertinence de la phrase. - -Le sport fait partie aussi des domaines explorés avec le RàPC, qui est généralement employé pour suggérer des routines d'entraînement, prédire des temps de course, ainsi comme évaluer et améliorer la performance des athlètes. La prédiction du temps de course pour un athlète est l'objectif du travail de \cite{10.1007/978-3-030-01081-2_25} où les coureurs ont été suivis et analysés pour la prédictions des temps de finalisation de une course, ici est implémenté un algorithme pour ceux qui font de la marathon en utilisant l'algorithme KNN pour rechercher les cas similaires et en faisant la prédiction avec la moyenne pondérée des meilleurs temps d'arrivée des cas similaires retrouvés dans la première étape du RàPC. Tandis que \cite{10.1007/978-3-030-58342-2_8} essaie de prédire le meilleur temps personnel pour des patineurs en se basant sur l'analogie de que les patineur avec des caractéristiques et histoire de course similaires auront des temps similaires aussi, mais parfois calculer une moyenne des temps similaires trouvés, ne suffit pas, car il y a des variables qui peuvent changer beaucoup la performance du patineur comme: le type de course, une piste spécifique, la distance de course, etc; l'algorithme a été testé avec une base de données qui contiens l'information de 21 courses de 500m, 700m, 1000m, 1500m, 3km, 5km and 10km entre Septembre 2015 et Janvier 2020. Un système multi-fonctionnel apparait dans \cite{10.1007/978-3-030-58342-2_5} car il permet de obtenir une prédiction du temps de course, suggérer un plan du rythme de la course et il recommande aussi un plan d'entraînement pour une course donnée; les trois fonctionnalités sont implémentées avec RàPC et en utilisant 'information des coureurs qui présentent un parcours historique et des caractéristiques physiques similaires, les plans d'entraînement ont été définis génériquement sur 16 semaines avant le début de la marathon parce que c'est le temps usuel pour la préparation de tous les coureurs. Le système a été évalué avec une base de données de 21000 coureurs dans la période de 2014 à 2017 qui ont couru les marathons de Dublin, Londres ou New-York. - -Les systèmes de recommandation et le RàPC peuvent aussi être combinés comme le fait \cite{Obeid}, dont le objectif est d'arriver à minimiser les limitations des systèmes traditionnels ainsi comme avoir la capacité d'analyser des données hétérogènes et dans des grands espaces dimensionnels, dans ce travail est recommandé aux élèves du secondaire le parcours de carrière et les universités/collèges qui correspondent à leurs intérêts, ce travail montre aussi une taxonomie des techniques algorithmiques généralement utilisées dans les systèmes de recommandation pour les EIAH (figure \figref{figTax}). La utilisation du RàPC dans les environnements informatiques pour l'apprentissage humain (EIAH) et la personnalisation montrent des résultats positifs comme l'indique le travail de \cite{8495930} dont le modèle suit le cycle traditionnel du RàPC en combinant les modèles d'apprentissage traditionnel et digital, les principales contributions sont: la représentation des cas et la recommandation des parcours d'apprentissage personnalisés selon l'information des autres apprenants. Pour démontrer l'effectivité du modèle est crée une base de cas initiales avec laquelle est recommandé le parcours à 120 apprenants, les résultats ont été obtenus avec l'aide des examens avant et après avoir suivi le parcours recommandé par le modèle. - -\mfigure[!ht]{width=\textwidth}{./Figures/taxonomieEIAH.png}{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (Traduit de \cite{Obeid})}{figTax} - -%\begin{figure} -%\centering -%\includegraphics[scale=21]{./Figures/taxonomieEIAH.png} -%\caption{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (\cite{Obeid})} -%\label{figTax} -%\end{figure} - -Certains travaux ont appliqué le raisonnement à partir de cas à un problème spécifique en proposant des représentations des cas et des solutions différents et d'autres ont modifié le cycle conceptuel comme est montré dans la figure \figref{figMCBR2} extraite de \cite{10.1007/978-3-319-47096-2_11} où est additionné un autre cycle complémentaire avec l'idée d'améliorer le résultat du processus du RàPC en utilisant le \textit{Deep Learning}; dans \cite{10.1007/978-3-642-15973-2_50} la phase de stockage est modifiée en retenant les cas dont les résultats n'ont pas eu de succès, pour guider le processus dans la fabrications de nouvelles pièces; \cite{Robertson2014ARO} additionne pour chaque cas, une valeur d'utilité espérée selon chaque possible action avec laquelle est possible d'obtenir une prédiction probabiliste de l'application des actions à un état initiale donné. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR2.png}{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (Traduit de \cite{10.1007/978-3-319-47096-2_11})}{figMCBR2} - -Le tableau \ref{tabArts2} montre un récapitulatif des articles analysés dans l'état de l'art du RàPC. - -\begin{table} -\footnotesize -\begin{tabular}{p{4.2cm}p{9cm}} -Ref&Limites et faiblesses\\ -\hline - -\cite{PETROVIC201617}&Grand quantité de données pour que l'algorithme marche bien. Recommandation limitée à un problème très spécifique\\ - -\cite{ROLDANREYES20151}&Un seul méthode d'adaptation est utilisé. Le modèle n'exploite pas les cas qui présentent une erreur\\ - -\cite{JUNG20095695}&Le modèle d'adaptation proposé marche bien seulement avec des cas très proches. L'apprentissage dans le RàCP se limite à stocker les nouveaux cas\\ - -\cite{10.1007/978-3-642-15973-2_50}&Le RàPC n'est pas modifié ou amélioré. La révision dans le RàPC n'est pas automatique\\ - -\cite{10.1007/978-3-319-47096-2_11}&Beaucoup de données nécessaires pour entrainer le modèle de 'Deep Learning'. Les solution générées n'ont pas été validées\\ - -\cite{10.1007/978-3-319-61030-6_1}&L'évaluation des solution générées n'est pas automatique. Les solutions sont produites avec un seul point de vue\\ - -\cite{Muller}&Un seul approche pour adapter les cas dans le RàPC. La révision dans le RàPC n'est pas automatique\\ - -\cite{10.1007/978-3-319-24586-7_20}&Les solutions générées peuvent présenter des incohérences. Il n y a pas une métrique objective pour mesurer la qualité des réponses\\ - -\cite{10.1007/978-3-030-58342-2_20}&Les résultats ne sont pas très bonnes. Le modèle n'a pas de connaissances linguistiques\\ - -\cite{10.1007/978-3-030-01081-2_25}&Les prédictions n'ont pas été testées dans le monde réel. Les données sont très spécifiques et peu variées\\ - -\cite{10.1007/978-3-030-58342-2_8}&Les prédictions sont pour des cas limités. Le modèle est entrainé pour un type d'utilisateur spécifique\\ - -\cite{10.1007/978-3-030-58342-2_5}&Seulement une technique de sélection de solutions. Les données nécessaires pour le modèle sont complexes\\ - -\cite{Obeid}&L'ontologie peut être limitée pour évaluer plusieurs cas inconnus ou imprévus. Il est nécessaire d'avoir une forte connaissance dans le domaine\\ - -\cite{8495930}&Le modèle a été validé avec peu de données. Les recommandation se basent seulement dans l'information des autres apprenants\\ - -\end{tabular} -\caption{Tableau de synthèse des articles analysés dans l’état de l’art du RàPC} -\label{tabArts2} -\end{table} - -%\begin{figure} -%\centering -%\includegraphics[scale=0.4]{./Figures/ModCBR2.png} -%\caption{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (\cite{10.1007/978-3-319-47096-2_11})} -%\label{figMCBR2} -%\end{figure} - -Deux problèmes très communes des systèmes de recommandation sont : le \textit{cold-start} qui occurs au début d'une recommandation lorsque il n'y a pas suffisamment de données pour calculer ou inférer une recommandation appropriée et le {gray-sheep} que se produit lorsque un utilisateur présente un comportement très différent des utilisateurs qui sont stockés dans la base de données et le système ne peut pas générer des recommandations en se basant sur l'information disponible. diff --git a/chapters/CBR_1.tex b/chapters/CBR_1.tex deleted file mode 100644 index 0db34ee..0000000 --- a/chapters/CBR_1.tex +++ /dev/null @@ -1,92 +0,0 @@ -\chapter{\'Etat de l'art (Raisonnement à Partir de Cas)} - -\section{Raisonnement à partir de cas (RàPC)} - -Le raisonnement à partir de cas est une approche fondé sur la connaissance, c'est une technique d'intelligence artificielle dont l'idée est de résoudre un nouveau problème donné en utilisant l'analogie grâce aux connaissances déjà acquises par le système. Le RàPC est apparu comme une alternative pour améliorer les systèmes experts. Initialement, en 1977, \colorbox{pink}{je ne comprends pas les dates} les travaux de Shank et Abelson \cite{schank+abelson77} sur l'organisation hiérarchique de la mémoire pour imiter le raisonnement humain ont permis d'implémenter un système \colorbox{pink}{à?} a Kolodner \cite{KOLODNER1983281} en l'année 1983 pour arriver au terme RàPC en 1989 dans le travail de Riesbeck et Shank \cite{Riesbeck1989}. - -Le raisonnement à partir de cas a été utilisé dans différents domaines comme : la médecine, la conception de produits,les sports, les systèmes de recommandation, etc. En médecine le RàPC a été appliqué pour explorer et aider la prise de décisions des médecins dans les traitements qui requièrent l'administration de doses précises selon les cas et les patients. \cite{PETROVIC201617} propose un algorithme de raisonnement à partir de cas qui indique quelle est la dose correcte de radiation pour le traitement du cancer. Pour administrer une dose de radiothérapie il est nécessaire de connaître avec précision deux paramètres: le nombre de faisceaux et l'angle de chacun d'eux. L'algorithme proposé essaie de trouver la valeur optimale pour la combinaison des deux paramètres en utilisant les réseaux de neurones et l'adaptation des cas connus pour modifier le nombre de faisceaux et adapter l'angle de chaque faisceau. La validation de l'algorithme est évaluée avec une base de 80 cas réels de cancer du cerveau extraits de l'hôpital de Nottingham City. Le nombre de neurones et de couches a été changé de façon empirique. Les résultats montrent que l'utilisation des cas historiques et la construction des solutions à partir des solutions déjà connues permet une amélioration de 12\% dans les cas \colorbox{pink}{pour le nombre de?}du nombre de faisceaux et 29\% \colorbox{pink}{pour le nombre de?}dans les cas de l'angle des faisceaux. - -Plusieurs travaux ont obtenu de bons \colorbox{pink}{meilleurs?}résultats en appliquant le RàPC grâce à des modifications dans chacune des phases ou en combinant différents algorithmes au design \colorbox{pink}{? conception }de produits. Dans \cite{ROLDANREYES20151} les auteurs proposent, comme le montre la figure \figref{figMCBR1}, un algorithme pour produire le propylène glycol dans un réacteur chimique. Dans ce cas la phase de réutilisation du RàPC est mélangée avec la recherche des états qui satisfassent le nouveau problème (Constraint satisfaction problems CSP) en utilisant l'information des cas de base déjà résolus. Les solutions trouvées sont évaluées selon le nombre de changements réalisés sur les solutions déjà connues (Parcimonie), le nombre de solutions possibles trouvées (Accuracy), l'évaluation de commentaires faits par des experts et la complexité des transformations réalisées. Il n'est pas obligatoire de modifier tout le cycle du RàPC pour l'adapter à une problématique spécifique, par exemple, dans \cite{JUNG20095695} les auteurs développent un système basé sur l'hybridation du RàPC avec des réseaux de neurones pour concevoir des produits. Le système se focalise uniquement dans les phases "rechercher" et "réutiliser" du RàPC dans lesquelles sont exécutés les algorithmes implémentés. Le système détermine de façon automatique les valeurs pour les paramètres nécessaires à la conception d'un produit particulier en suivant le cycle traditionnel du RàPC. Avec l'algorithme de k-moyennes, est extrait un cas représentatif de la base de cas et l'adaptation des solutions des voisins trouvées est faite avec le réseau de neurones RBFN. Avec 830 tests, \colorbox{pink} {je ne comprends pas la phrase qui suit}les nouveaux cas très similaires aux cas de la base initiale est obtenu un bon design en 99.4\% des cas, pour les cas dissemblables le pourcentage est de 94.8\%. Le RàPC peut également être utilisé comme outil secondaire pour aider d'autres algorithmes à trouver la bonne configuration. Dans \cite{10.1007/978-3-642-15973-2_50} un réseau de neurones classique est implémenté pour colorbox{pink} {désigner?}designer la géométrie d'une matrice pour l'extrusion de l'aluminium. En effet,actuellement c'est un processus qui se fait manuellement et par essai et erreur. Le RàPC est alors utilisé pour aider à déterminer les valeurs optimales des paramètres du réseau. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR1.png}{Cycle du RàPC modifié. (Traduit de \cite{ROLDANREYES20151})}{figMCBR1} - -%\begin{figure} -%\centering -%\includegraphics[scale=0.4]{./Figures/ModCBR1.png} -%\caption{Cycle du RàPC modifié. (\cite{ROLDANREYES20151})} -%\label{figMCBR1} -%\end{figure} - -L’un des problèmes représentatifs(\colorbox{pink}{avant:L'une des problématiques la plus représentative et la plus fondamentale} de l'utilisation du RàPC est la création de recettes de cuisine à partir d'une base d'ingrédients et d'un ensemble de recettes déjà connues. Malgré l'apparente simplicité de ce problème il existe beaucoup de variations (\colorbox{pink}{pourquoi variations ?}et de modèles. Dans \cite{10.1007/978-3-319-47096-2_11} cette problématique est abordée, Les auteurs ajoutent un nouveau cycle au cycle traditionnel du RàPC. Le premier cycle génère des descriptions abstraites du problème avec un réseau de neurones et des algorithmes génétiques; le second cycle prend les descriptions abstraites comme des nouveaux cas, cherche les cas similaires et adapte les solutions rencontrées. L'exécution des deux cycles prend en compte certains critères prédéfinis par l'utilisateur. En comparant le même problème avec le cycle traditionnel du RàPC il y a effectivement une amélioration de la qualité des recettes proposées et celles-ci sont plus en accord avec les critères définis. Sur la même problématique, mais plus focalisé sur la génération des recettes novatrices, \cite{10.1007/978-3-319-61030-6_1} utilise un modèle probabiliste qui considère les préférences des utilisateurs, un indicateur de curiosité et un composant (\colorbox{pink}{qui synthétise tout le design ??} . Une fois que toutes les exigences sont renseignées, la recette est créée en \colorbox{pink}{combinant les ingrédients avec un réseau d'apprentissage profond ??}. Les modifications dans la représentation des cas sont également une source d'amélioration des résultats. Cela car (\colorbox{pink}{je ne comprends pas la suite}parfois d'un côté il y a plus d'information et de l'autre certaines techniques ou algorithmes fonctionnent mieux. C'est le cas dans \cite{Muller} où les recettes sont codées comme un processus de transformation et mélangent des ingrédients en suivant une suite de pas (\colorbox{pink}{étapes ?} ordonnés. Pour créer des recettes innovantes, une mesure de distance est utilisée entre les ingrédients. Cette mesure permet de trouver une recette en changeant les ingrédients par des ingrédients proches. De la même façon il est possible de créer des recettes plus proches des exigences des utilisateurs. Les étapes de transformation appelées aussi opérateurs sont stockées et catégorisées grâce à une métrique permettant de les échanger afin d’obtenir une nouvelle recette. - -Une autre type d'application du RàPC très notable est le génération, analyse et correction du texte écrit; pour la réalisation de ces tâches parfois il est nécessaire de transformer le texte en représentation numérique ou établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires, en explorant les possibles transformations de forme que la nouvelle histoire ne soit pas très similaire aux histoire déjà connues mais que elle soit cohérent. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si la histoire générée corresponde avec les critères spécifiés ou si le cycle d'adaptation doit recommencer; l'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en unifiant tout avec des actions; la plus part des histoires générées sont cohérents mais sont des paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées, ce travail n'utilise pas la transformation numérique des phrases, ni connaissances linguistiques, mais seulement retrouve les phrases similaires en utilisant l'algorithme LCS (Longest Common Subsequence) et en calculant la distance parmi toutes les phrases pour savoir si elle est bien écrite ou pas, sinon le système peut proposer une correction en changeant certains mots selon le contexte et calculer à nouveau les distances qui déterminent la cohérence et pertinence de la phrase. - -Un autre type d'application du RàPC est la génération, l’analyse et la correction de textes écrits. Pour la réalisation de ces tâches il est parfois nécessaire de transformer le texte en représentation numérique ou d’établir une fonction ou mesure qui va indiquer quels mots ont une proximité sémantique. Le travail de \cite{10.1007/978-3-319-24586-7_20} utilise le RàPC pour générer des histoires en utilisant le texte d'autres histoires. Le système \colorbox{pink}{ok pour le système ?} explore les transformations de forme possibles afin que la nouvelle histoire ne soit pas similaire aux histoires déjà connues mais qu’elle soit cohérente. Le travail est plus focalisé sur la phase de révision, car les résultats de la révision déterminent si l’histoire générée correspond aux critères spécifiés ou si le cycle d'adaptation doit être recommencé. L'adaptation se fait en cherchant dans la base des cas, les personnages, les contextes, les objets et en \colorbox{pink}{unifiant tout avec des actions ??}. La plupart des histoires générées sont cohérents mais sont des paragraphes très courts. Dans \cite{10.1007/978-3-030-58342-2_20} les phrases écrites en langue française sont corrigées, ce travail n'utilise ni la transformation numérique des phrases, ni des connaissances linguistiques. Ils retrouvent des phrases similaires en utilisant l'algorithme LCS (Longest Common Subsequence) et en calculant la distance entre toutes les phrases pour savoir \colorbox{pink}{je ne comprends pas la suite }si elle est bien écrite ou pas. Si la phrase n’est pas bien écrite, le système peut proposer une correction en changeant certains \colorbox{pink}{ots ?} ots selon le contexte et calculer à nouveau les distances qui déterminent la cohérence et pertinence de la phrase. - -Le sport fait également partie des domaines explorés par le RàPC, qui est généralement employé pour suggérer des routines d'entraînement, prédire des temps de course, ainsi qu’évaluer et améliorer la performance des athlètes. L'objectif du travail de \cite{10.1007/978-3-030-01081-2_25} est la prédiction du temps de course pour un athlète . \colorbox{pink}{où les coureurs ont été suivis et analysés pour la prédictions des temps de finalisation de une course, ?}. L’algorithme implémenté pour une course de marathon utilise l'algorithme KNN pour rechercher les cas similaires. Il prédit grâce à la moyenne pondérée des meilleurs temps d'arrivée des cas similaires retrouvés dans la première étape du RàPC. Dans \cite{10.1007/978-3-030-58342-2_8} les auteurs essaient de prédire le meilleur temps personnel pour des patineurs en se basant sur l'analogie avec des patineurs ayant des caractéristiques et une histoire de course similaires\colorbox{pink}{pas sûre de la structure de cette phrase}. Cependant, parfois calculer une moyenne des temps similaires trouvés ne suffit pas, car il y a des variables qui peuvent beaucoup changer la performance du patineur comme: le type de course, une piste spécifique, la distance de course, etc. L'algorithme a été testé avec une base de données qui contient l'information de 21 courses de 500m, 700m, 1000m, 1500m, 3km, 5km and 10km entre Septembre 2015 et Janvier 2020. Un système multi-fonctionnel apparait \colorbox{pink}{est décrit dans }dans \cite{10.1007/978-3-030-58342-2_5}, il permet d’obtenir une prédiction du temps de course, de suggérer un plan du rythme de la course et il recommande également un plan d'entraînement pour une course donnée. Les trois fonctionnalités sont implémentées avec RàPC et en utilisant \colorbox{pink}{je ne comprends pas la suite}'information des coureurs qui présentent un parcours historique et des caractéristiques physiques similaires, les plans d'entraînement ont été définis génériquement sur 16 semaines avant le début du marathon parce que c'est le temps usuel pour la préparation de tous les coureurs. Le système a été évalué avec une base de données de 21000 coureurs des marathons de Dublin, Londres ou New-York pour la période de 2014 à 2017. - - -Les systèmes de recommandation et le RàPC peuvent aussi être combinés comme le font \cite{Obeid}, dont l’objectif est de minimiser les limitations des systèmes traditionnels ainsi que d’être capable d'analyser des données hétérogènes \colorbox{pink}{pas sûre de la suite} dans des grands espaces dimensionnels. Dans ce travail, le parcours de carrière et les universités/collèges est recommandé aux élèves du secondaire en fonction de leurs intérêts. Ce travail montre également une taxonomie des techniques algorithmiques généralement utilisées dans les systèmes de recommandation pour les EIAH (figure \figref{figTax}). L’utilisation du RàPC dans les environnements informatiques pour l'apprentissage humain (EIAH) et la personnalisation montrent des résultats positifs comme l'indique le travail de \cite{8495930}. Le modèle proposé suit le cycle traditionnel du RàPC en combinant les modèles d'apprentissage traditionnel et digital \colorbox{pink}{numérique ?}. Les principales contributions sont: la représentation des cas et la recommandation des parcours d'apprentissage personnalisés selon les informations issues des autres apprenants. Pour démontrer l'effectivité \colorbox{pink}{efficacité ?}du modèle, une base de cas initiaux a été créée. Celle-ci permet la recommandation du parcours de 120 apprenants. Les résultats ont été obtenus \colorbox{pink}{grâce à des examens}avec l'aide des examens avant et après avoir suivi le parcours recommandé par le modèle. - -\mfigure[!ht]{width=\textwidth}{./Figures/taxonomieEIAH.png}{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (Traduit de \cite{Obeid})}{figTax} - -%\begin{figure} -%\centering -%\includegraphics[scale=21]{./Figures/taxonomieEIAH.png} -%\caption{Taxonomie des techniques algorithmiques employées pour des modules de recommandation dans les EIAH (\cite{Obeid})} -%\label{figTax} -%\end{figure} - -Certains travaux ont appliqué le raisonnement à partir de cas à un problème spécifique en proposant des représentations des cas et des solutions différentes, d'autres ont modifié le cycle conceptuel comme le montre la figure \figref{figMCBR2} extraite de \cite{10.1007/978-3-319-47096-2_11}. Dans cette proposition, un cycle complémentaire est additionné avec l'idée d'améliorer le résultat du processus du RàPC en utilisant le \textit{Deep Learning}. Dans \cite{10.1007/978-3-642-15973-2_50} la phase de stockage est modifiée en retenant les cas dont les résultats n'ont pas eu de succès, pour guider le processus dans la fabrications de nouvelles pièces. Enfin, dans \cite{Robertson2014ARO} les auteurs proposent \colorbox{pink}{d’ajouter} additionne pour chaque cas, une valeur d'utilité espérée selon chaque action possible avec laquelle il est possible d'obtenir une prédiction probabiliste de l'application des actions à un état initiale donné \colorbox{pink}{à revoir}. - -\mfigure[!ht]{width=\textwidth}{./Figures/ModCBR2.png}{Ajout d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (Traduit de \cite{10.1007/978-3-319-47096-2_11})}{figMCBR2} - -Le tableau \ref{tabArts2} montre un récapitulatif des articles analysés dans l'état de l'art du RàPC. - -\begin{table} -\footnotesize -\begin{tabular}{p{4.2cm}p{9cm}} -Ref&Limites\\ -\hline - -\cite{PETROVIC201617}&Grande quantité de données pour que l'algorithme fonctionne correctement. Recommandation limitée à un problème très spécifique.\\ - -\cite{ROLDANREYES20151}&Une seule méthode d'adaptation est utilisée. Le modèle n'exploite pas les cas qui présentent une erreur.\\ - -\cite{JUNG20095695}&Le modèle d'adaptation proposé fonctionne seulement avec des cas très proches. L'apprentissage dans le RàCP se limite à stocker les nouveaux cas.\\ - -\cite{10.1007/978-3-642-15973-2_50}&Le RàPC n'est pas modifié ou amélioré. La révision dans le RàPC n'est pas automatique\\ - -\cite{10.1007/978-3-319-47096-2_11}&Beaucoup de données sont nécessaires pour entrainer le modèle de 'Deep Learning'. Les solutions générées n'ont pas été validées.\\ - -\cite{10.1007/978-3-319-61030-6_1}&L'évaluation des solutions générées n'est pas automatique. Les solutions sont produites avec un seul point de vue.\\ - -\cite{Muller}&Une seule approche pour adapter les cas dans le RàPC. La révision dans le RàPC n'est pas automatique.\\ - -\cite{10.1007/978-3-319-24586-7_20}&Les solutions générées peuvent présenter des incohérences. Il n'y a pas une métrique objective pour mesurer la qualité des réponses.\\ - -\cite{10.1007/978-3-030-58342-2_20}&Les résultats ne sont pas très bons. Le modèle n'a pas de connaissances linguistiques.\\ - -\cite{10.1007/978-3-030-01081-2_25}&Les prédictions n'ont pas été testées dans le monde réel. Les données sont très spécifiques et peu variées.\\ - -\cite{10.1007/978-3-030-58342-2_8}&Les prédictions sont pour des cas limités. Le modèle est entrainé pour un type d'utilisateur spécifique.\\ - -\cite{10.1007/978-3-030-58342-2_5}&Seulement une technique de sélection de solutions. Les données nécessaires pour le modèle sont complexes.\\ - -\cite{Obeid}&L'ontologie peut être \colorbox{pink}{limitée?}limitée pour évaluer plusieurs cas inconnus ou imprévus. Il est nécessaire d'avoir une forte connaissance du domaine.\\ - -\cite{8495930}&Le modèle a été validé avec peu de données. Les recommandations se basent seulement sur l'information des autres apprenants.\\ - -\end{tabular} -\caption{Tableau de synthèse des articles analysés dans l’état de l’art du RàPC} -\label{tabArts2} -\end{table} - -%\begin{figure} -%\centering -%\includegraphics[scale=0.4]{./Figures/ModCBR2.png} -%\caption{Addition d'un cycle complémentaire avec \textit{Deep Learning} au RàPC (\cite{10.1007/978-3-319-47096-2_11})} -%\label{figMCBR2} -%\end{figure} - -Deux problèmes très communs des systèmes de recommandation sont : (1)le \textit{cold-start} qui se produit au début d'une recommandation lorsque il n'y a pas suffisamment de données pour calculer ou inférer une recommandation appropriée. Et (2) le \textit{gray-sheep} qui se produit lorsqu'un utilisateur présente un comportement très différent de ceux qui sont stockés dans la base de données. 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