El Raonament Basat en Casos (CBR) és un paradigma d'aprenentatge basat en establir analogies amb problemes prèviament resolts per resoldre'n de nous. Per tant, l'organització, l'accés i la utilització del coneixement previ són aspectes claus per tenir èxit en aquest procés. No obstant, la majoria dels problemes reals presenten grans volums de dades complexes, incertes i amb coneixement aproximat i, conseqüentment, el rendiment del CBR pot veure's minvat degut a la complexitat de gestionar aquest tipus de coneixement. Això ha fet que en els últims anys hagi sorgit una nova línia de recerca anomenada Soft-Computing and Intelligent Information Retrieval enfocada en mitigar aquests efectes. D'aquí neix el context d'aquesta tesi.Dins de l'ampli ventall de tècniques Soft-Computing per tractar coneixement complex, els Mapes Autoorganitzatius (SOM) destaquen sobre la resta per la seva capacitat en agrupar les dades en patrons, els quals permeten detectar relacions ocultes entre les dades. Aquesta capacitat ha estat explotada en treballs previs d'altres investigadors, on s'ha organitzat la memòria de casos del CBR amb SOM per tal de millorar la recuperació dels casos.La finalitat de la present tesi és donar un pas més enllà en la simple combinació del CBR i de SOM, de tal manera que aquí s'introdueixen les capacitats de Soft-Computing i de Knowledge Discovery de SOM en totes les fases del CBR per nodrir-les del nou coneixement descobert. A més a més, les mètriques de complexitat apareixen en aquest context com un instrument precís per modelar el funcionament de SOM segons la tipologia de les dades. L'assoliment d'aquesta integració es pot dividir principalment en quatre fites: (1) la definició d'una metodologia per determinar la millor manera de recuperar els casos tenint en compte la complexitat de les dades i els requeriments de l'usuari; (2) la millora de la fiabilitat de la proposta de solucions gràcies a les relacions entre els clústers i els casos; (3) la potenciació de les capacitats explicatives mitjançant la generació d'explicacions simbòliques; (4) el manteniment incremental i semi-supervisat de la memòria de casos organitzada per SOM.Tots aquests punts s'integren sota la plataforma SOMCBR, la qual és extensament avaluada sobre datasets provinents de l'UCI Repository i de dominis mèdics i telemàtics.Addicionalment, la tesi aborda de manera secundària dues línies de recerca fruït dels requeriments dels projectes on ha estat ubicada. D'una banda, s'aborda la definició de funcions de similitud específiques per definir com comparar un cas resolt amb un de nou mitjançant una variant de la Computació Evolutiva anomenada Evolució de Gramàtiques (GE). D'altra banda, s'estudia com definir esquemes de cooperació entre sistemes heterogenis per millorar la fiabilitat de la seva resposta conjunta mitjançant GE. Ambdues línies són integrades en dues plataformes, BRAIN i MGE respectivament, i són també avaluades amb els datasets anteriors. / El Razonamiento Basado en Casos (CBR) es un paradigma de aprendizaje basado en establecer analogías con problemas previamente resueltos para resolver otros nuevos. Por tanto, la organización, el acceso y la utilización del conocimiento previo son aspectos clave para tener éxito. No obstante, la mayoría de los problemas presentan grandes volúmenes de datos complejos, inciertos y con conocimiento aproximado y, por tanto, el rendimiento del CBR puede verse afectado debido a la complejidad de gestionarlos. Esto ha hecho que en los últimos años haya surgido una nueva línea de investigación llamada Soft-Computing and Intelligent Information Retrieval focalizada en mitigar estos efectos. Es aquí donde nace el contexto de esta tesis.Dentro del amplio abanico de técnicas Soft-Computing para tratar conocimiento complejo, los Mapas Autoorganizativos (SOM) destacan por encima del resto por su capacidad de agrupar los datos en patrones, los cuales permiten detectar relaciones ocultas entre los datos. Esta capacidad ha sido aprovechada en trabajos previos de otros investigadores, donde se ha organizado la memoria de casos del CBR con SOM para mejorar la recuperación de los casos.La finalidad de la presente tesis es dar un paso más en la simple combinación del CBR y de SOM, de tal manera que aquí se introducen las capacidades de Soft-Computing y de Knowledge Discovery de SOM en todas las fases del CBR para alimentarlas del conocimiento nuevo descubierto. Además, las métricas de complejidad aparecen en este contexto como un instrumento preciso para modelar el funcionamiento de SOM en función de la tipología de los datos. La consecución de esta integración se puede dividir principalmente en cuatro hitos: (1) la definición de una metodología para determinar la mejor manera de recuperar los casos teniendo en cuenta la complejidad de los datos y los requerimientos del usuario; (2) la mejora de la fiabilidad en la propuesta de soluciones gracias a las relaciones entre los clusters y los casos; (3) la potenciación de las capacidades explicativas mediante la generación de explicaciones simbólicas; (4) el mantenimiento incremental y semi-supervisado de la memoria de casos organizada por SOM. Todos estos puntos se integran en la plataforma SOMCBR, la cual es ampliamente evaluada sobre datasets procedentes del UCI Repository y de dominios médicos y telemáticos.Adicionalmente, la tesis aborda secundariamente dos líneas de investigación fruto de los requeri-mientos de los proyectos donde ha estado ubicada la tesis. Por un lado, se aborda la definición de funciones de similitud específicas para definir como comparar un caso resuelto con otro nuevo mediante una variante de la Computación Evolutiva denominada Evolución de Gramáticas (GE). Por otro lado, se estudia como definir esquemas de cooperación entre sistemas heterogéneos para mejorar la fiabilidad de su respuesta conjunta mediante GE. Ambas líneas son integradas en dos plataformas, BRAIN y MGE, las cuales también son evaluadas sobre los datasets anteriores. / Case-Based Reasoning (CBR) is an approach of machine learning based on solving new problems by identifying analogies with other previous solved problems. Thus, organization, access and management of this knowledge are crucial issues for achieving successful results. Nevertheless, the major part of real problems presents a huge amount of complex data, which also presents uncertain and partial knowledge. Therefore, CBR performance is influenced by the complex management of this knowledge. For this reason, a new research topic has appeared in the last years for tackling this problem: Soft-Computing and Intelligent Information Retrieval. This is the point where this thesis was born.Inside the wide variety of Soft-Computing techniques for managing complex data, the Self-Organizing Maps (SOM) highlight from the rest due to their capability for grouping data according to certain patterns using the relations hidden in data. This capability has been used in a wide range of works, where the CBR case memory has been organized with SOM for improving the case retrieval.The goal of this thesis is to take a step up in the simple combination of CBR and SOM. This thesis presents how to introduce the Soft-Computing and Knowledge Discovery capabilities of SOM inside all the steps of CBR to promote them with the discovered knowledge. Furthermore, complexity measures appear in this context as a mechanism to model the performance of SOM according to data topology. The achievement of this goal can be split in the next four points: (1) the definition of a methodology for setting up the best way of retrieving cases taking into account the data complexity and user requirements; (2) the improvement of the classification reliability through the relations between cases and clusters; (3) the promotion of the explaining capabilities by means of the generation of symbolic explanations; (4) the incremental and semi-supervised case-based maintenance. All these points are integrated in the SOMCBR framework, which has been widely tested in datasets from UCI Repository and from medical and telematic domains. Additionally, this thesis secondly tackles two additional research lines due to the requirements of a project in which it has been developed. First, the definition of similarity functions ad hoc a domain is analyzed using a variant of the Evolutionary Computation called Grammar Evolution (GE). Second, the definition of cooperation schemes between heterogeneous systems is also analyzed for improving the reliability from the point of view of GE. Both lines are developed in two frameworks, BRAIN and MGE respectively, which are also evaluated over the last explained datasets.

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is 'aware' of users' preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users' verbal intentions in conversations while making recommendation decision. One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain. Cobot leverages these interactions to maintain users' episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user's interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation. The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.

Community based service-learning is one of the fastest growing reforms in higher education, especially in the field of health care. The increased interest in this phenomenon is based on the demands by government and society that higher education institutions should be more responsive to the needs of the community. Literature, however, reflects that service learning lacks a sound theoretical base to guide teaching and learning due to limited research in this area. This study was, therefore, aimed at exploring the phenomenon knowledge construction in basic nursing programmes in selected South African nursing schools with the intention to generate a middle range theory that may be used to guide the process of knowledge construction in community-based service-learning programmes. This study adopted a qualitative approach and a grounded theory research design by Strauss and Corbin. Two university-based schools of nursing were purposively selected to participate in the study. There were a total number of 16 participants. The collection of data was intensified by the use of multiple sources of data (participant observation, documents analysis and in-depth structured interviews). The data analysis process entailed three phases; open, axial and selective coding. The results of the study revealed that the phenomenon “knowledge construction” is conceptualised as having specific core characteristics, which include the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue and communities of learners. The findings showed that there are a number of antecedent conditions and contextual circumstances contributing to how knowledge is constructed in a community based service learning programme. The process of knowledge construction emerged as cyclical in nature, with students, facilitators and community members having specific roles to play in the process. A number of intervening variables were identified that had an influence on the expected outcomes on knowledge construction in community based service learning programmes. These findings led to the generation of a conceptual model. Knowledge construction according to this model takes place in an environment which is characterised by interactive learning, collaborative learning, actively learning and inquiry-based learning through continuous reflective learning processes. The main concepts in this conceptual model include concrete learning experiences, continuous reflection, problem posing, problem analysis, knowledge deconstruction and knowledge generation, knowledge verification, knowledge generation, testing of generated knowledge and evaluation of generated knowledge. The sub-concepts include learning through senses, an initial situation, health-related triggers, social interaction, reflection-in action, reflection-on action, hypotheses generation, conceptualisation of learning experiences, information validation and community interventions. Recommendations were categorised into education and training of academic staff, application of the model and further research with regard to quality assurance in CBSL programmes as well as the use of other research designs for similar studies. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2011.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

El test de circuits és una fase del procés de producció que cada vegada pren més importància quan es desenvolupa un nou producte. Les tècniques de test i diagnosi per a circuits digitals han estat desenvolupades i automatitzades amb èxit, mentre que aquest no és encara el cas dels circuits analògics. D'entre tots els mètodes proposats per diagnosticar circuits analògics els més utilitzats són els diccionaris de falles. En aquesta tesi se'n descriuen alguns, tot analitzant-ne els seus avantatges i inconvenients.Durant aquests últims anys, les tècniques d'Intel·ligència Artificial han esdevingut un dels camps de recerca més importants per a la diagnosi de falles. Aquesta tesi desenvolupa dues d'aquestes tècniques per tal de cobrir algunes de les mancances que presenten els diccionaris de falles. La primera proposta es basa en construir un sistema fuzzy com a eina per identificar. Els resultats obtinguts son força bons, ja que s'aconsegueix localitzar la falla en un elevat tant percent dels casos. Per altra banda, el percentatge d'encerts no és prou bo quan a més a més s'intenta esbrinar la desviació.Com que els diccionaris de falles es poden veure com una aproximació simplificada al Raonament Basat en Casos (CBR), la segona proposta fa una extensió dels diccionaris de falles cap a un sistema CBR. El propòsit no és donar una solució general del problema sinó contribuir amb una nova metodologia. Aquesta consisteix en millorar la diagnosis dels diccionaris de falles mitjançant l'addició i l'adaptació dels nous casos per tal d'esdevenir un sistema de Raonament Basat en Casos. Es descriu l'estructura de la base de casos així com les tasques d'extracció, de reutilització, de revisió i de retenció, fent èmfasi al procés d'aprenentatge.En el transcurs del text s'utilitzen diversos circuits per mostrar exemples dels mètodes de test descrits, però en particular el filtre biquadràtic és l'utilitzat per provar les metodologies plantejades, ja que és un dels benchmarks proposats en el context dels circuits analògics. Les falles considerades son paramètriques, permanents, independents i simples, encara que la metodologia pot ser fàcilment extrapolable per a la diagnosi de falles múltiples i catastròfiques. El mètode es centra en el test dels components passius, encara que també es podria extendre per a falles en els actius. / Testing circuits is a stage of the production process that is becoming more and more important when a new product is developed. Test and diagnosis techniques for digital circuits have been successfully developed and automated. But, this is not yet the case for analog circuits. Even though there are plenty of methods proposed for diagnosing analog electronic circuits, the most popular are the fault dictionary techniques. In this thesis some of these methods, showing their advantages and drawbacks, are analyzed.During these last decades automating fault diagnosis using Artificial Intelligence techniques has become an important research field. This thesis develops two of these techniques in order to fill in some gaps in fault dictionaries techniques. The first proposal is to build a fuzzy system as an identification tool. The results obtained are quite good, since the faulty component is located in a high percentage of the given cases. On the other hand, the percentage of successes when determining the component's exact deviation is far from being good.As fault dictionaries can be seen as a simplified approach to Case-Based Reasoning, the second proposal extends the fault dictionary towards a Case Based Reasoning system. The purpose isnot to give a general solution, but to contribute with a new methodology. This second proposal improves a fault dictionary diagnosis by means of adding and adapting new cases to develop aCase Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken.Several circuits are used to show examples of the test methods described throughout the text. But, in particular, the biquadratic filter is used to test the proposed methodology because it isdefined as one of the benchmarks in the analog electronic diagnosis domain. The faults considered are parametric, permanent, independent and simple, although the methodology can be extrapolated to catastrophic and multiple fault diagnosis. The method is only focused and tested on passive faulty components, but it can be extended to cover active devices as well.

Les médecins cherchent à résoudre les problèmes de santé posés par des individus. Une solution individualisée tient compte de la singularité du patient concerné. L'individualisation des pratiques est-elle possible et souhaitable? Le cas échéant, selon quelles modalités peut-elle ou doit-elle être réalisée'? La première partie de la thèse vise à montrer: que la question se pose depuis les premières théories de la décision médicale (Hippocrate) ; qu'elle s'est posée de façon aiguë au début du XIX" siècle, avec l'apparition des études statistiques; et que l'observation médicale et son évolution concrétisent la façon dont la documentation des cas et leur individualisation interagissent. La deuxième partie reprend la question dans le contexte contemporain, à travers la naissance de l'"evidence-based medicine", ses critiques et son évolution. La troisième partie montre que l'articulation du raisonnement par règles et du raisonnement par cas modélise de façon opérationnelle une démarche raisonnée d'individualisation des décisions médicales. Ce modèle simple permet de rendre compte du mouvement d'aller-retour entre deux conceptions de l'individualisation et d'en proposer une version équilibrée, mise à l'épreuve dans les domaines de l'évaluation des pratiques et de la littérature médicale. / Physicians try to solve health problems of individual patients. Customized solutions take into account the uniqueness of the patient. Is the individualization of medical decisions possible and desirable'? If so, how can I tor should it be performed? The first part of the thesis shows: that the question arises since the first conceptualizations of medical reasoning (Hippocrates); that is was much debated in the early nineteenth century, when statistical studies were first performed to guide medical decisions; and that the medical observation and its evolution materialize how case documentation and management interact. The second part addresses the issue in the current context, from the birth of evidence-based medicine, its cri tics and its evolution. The third part shows that linking rule-based and case-based reasoning adequately pictures the process of customizing medical decisions. This simple model can account for the movement between two kinds of customization and leads to a balanced approach, tested in the field of practice evaluation and medical literature.

This paper aims to provide an Interactive Learning Environment using the computer to support aid in the diagnosis and treatment of patients with neurological disorders. The study proposes an architecture which facilitates the activities of students in the health area, in decision making, for the advice of physiotherapy for stroke patients. It was used the approach of Case-Based Reasoning (CBR) that has, like general idea, the use of past experiences to the solution of new problems. This work focused on the stages of indexing, representation and retrieval of cases, with the use of metrics, similar characteristics as the Count Features and Tversky s Contrast Model. A prototype was built for the validation of these metrics, proving the efficiency in the recovery of the cases on the basis of cases / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho tem como objetivo apresentar um Ambiente Interativo de Aprendizagem utilizando o Computador de apoio ao diagnóstico e auxílio no tratamento de pacientes que apresentam disfunções neurológicas. A pesquisa propõe uma arquitetura que facilite as atividades dos estudantes da área da saúde, na tomada de decisão, para o aconselhamento fisioterápico dos pacientes de acidente vascular cerebral. Utilizou-se a abordagem de Raciocínio Baseado em Casos (RBC) que tem como idéia geral a utilização de experiências passadas para a solução de novos problemas. Este trabalho se concentrou nas fases de indexação, representação e recuperação dos casos, com a utilização de métricas de similaridade como a Contagem de Características e a Regra do Contraste de Tversky. Um protótipo foi construído para a validação dessas métricas, provando a eficiência na recuperação dos casos na base de casos

This work aims to propose a recommendation model of prescription items for Pediatric Oncology based on data extraction from Electronic Patient Record. These data are used as indexed cases to aid providers of medical service based on the similarity of prescriptions, according to the patient's history. From the viewpoint of aid medical education, the modeling objective support the student or health professional in understanding the decision-making process during the prescription items oncological medical treatment, for example drugs, laboratory exams or images exams, diet, gases, care, chemotherapy, radiotherapy. To develop the model, was used the approach of Case Based Reasoning (CBR), through the representation of prescription medical base-case, indexed by their treatment items. During the recovery phase of cases, we used the tool. Data Mining by applying the model of association rule, together with the algorithm "apriori" for obtaining the similarity between cases. To update the case base, a procedure database for performing the process of Extraction, Transformation and Load of the database was developed. The developed model was applied in the database of the Electronic Patient Record of the Santa Casa de Misericordia de Maceio, based on Hospital Management Systems “MV Sistemas”, deployed in the unit since 2005. For the presentation of results, was used the Oracle Data Miner tool, which allowed access to the database and analysis of selected cases by identifying key words contained in the evolution of the clinical condition of the patient. The application of the experiments validate the occurrence of allowed combined application of items of treatment according to the keywords, which can be used as input in the process of making medical decision and tutoring. / Este trabalho tem como objetivo propor um Modelo de Recomendação de itens de prescrição para Oncologia Pediátrica baseado na extração de dados do Prontuário Eletrônico do Paciente. Esses dados são utilizados como casos indexados, para auxiliar os prestadores de serviço médico baseados na similaridade de prescrições, de acordo com o histórico do paciente. Do ponto de vista do apoio a educação médica, a modelagem objetiva apoiar o estudante ou o profissional de saúde no entendimento do processo de tomada de decisão durante a fase de prescrição de itens de tratamento médico oncológico, como, por exemplo: medicamentos, exames de laboratório ou de imagens, dieta, gases, cuidados, quimioterapia, radioterapia. Para o desenvolvimento do modelo, utilizou-se a abordagem de Raciocínio Baseado em Casos (RBC), através da representação de uma base de casos de prescrição médica, indexada por seus itens de tratamento. Durante a fase de recuperação de casos, utilizou-se a ferramenta de Mineração de Dados aplicando-se o modelo de regra de associação, em conjunto com o algoritmo “apriori” visando a obtenção da similaridade entre casos. Para a atualização da base de casos, foi desenvolvido um procedimento de banco de dados para execução do processo de Extração, Transformação e Carga da base de dados. O modelo desenvolvido foi aplicado na base de dados do Prontuário Eletrônico do Paciente da Santa Casa de Misericórdia de Maceió, baseado no sistema de gestão hospitalar MV Sistemas, implantado na unidade desde 2005. Para a apresentação dos resultados, utilizou-se a ferramenta Oracle Data Miner, que possibilitou o acesso ao banco de dados e a análise dos casos selecionados pela identificação de palavras chaves contidas na evolução do estado clínico do paciente. A aplicação dos experimentos permitiu validar a ocorrência de aplicação conjunta de itens de tratamento de acordo com as palavras chaves, o que pode ser utilizado como elemento para o processo de tomada de decisão médica e tutoria.

Dans ce travail, une étude est réalisée sur le développement d'une méthodologie permettant la génération et l'évaluation de nouvelles trajectoires de valorisation pour des déchets. Ainsi, pour répondre à cette problématique, trois sous problèmes ont été identifiés. Le premier concerne un cadre de modélisation permettant la représentation structurée et homogène de chaque trajectoire, ainsi que les indicateurs choisis pour l'évaluation de ces dernières, permettant une sélection ultérieure. Le deuxième se concentre sur le développement d'une méthodologie puis la réalisation d'un outil permettant la génération de nouvelles trajectoires en s'appuyant sur d'autres connues. Enfin, le dernier sous problème concerne le développement d'un second outil développé pour modéliser et estimer les trajectoires générées. La partie de création d'un cadre de modélisation cherche à concevoir des structures globales qui permettent la catégorisation des opérations unitaires sous plusieurs niveaux. Trois niveaux de décomposition ont été identifiés. La Configuration générique de plus haut niveau, qui décrit la trajectoire sous de grandes étapes de modélisation. Le second niveau, Traitement générique propose des ensembles de structures génériques de traitement qui apparaissent régulièrement dans les trajectoires de valorisation. Enfin, le plus bas niveau se focalise sur la modélisation des opérations unitaires. Un second cadre a été créé, plus conceptuel et comportant deux éléments : les blocs et les systèmes. Ces cadres sont ensuite accompagnés par un ensemble d'indicateurs choisis à cet effet. Dans une volonté d'approche de développement durable, un indicateur est sélectionné pour chacune de des composantes : économique, environnemental et social. Dans notre étude, l'impact social se limite à l'estimation du nombre d'emplois créés. Afin de calculer cet indicateur, une nouvelle approche se basant sur les résultats économiques d'une entreprise a été proposée et validée.L'outil de génération de nouvelles trajectoires s'appuie sur l'utilisation de la connaissance en utilisant un système de raisonnement à partir de cas (RàPC). Pour être adapté à notre problématique, la mise en œuvre de ce dernier a impliqué la levée de plusieurs points délicats. Tout d'abord, la structuration des données et plus largement la génération de cas sources sont réalisées par un système basé sur des réseaux sémantiques et l'utilisation de mécanismes d'inférences. Le développement d'une nouvelle méthode de mesure de similarité est réalisé en introduisant la notion de définition commune qui permet de lier les états, qui sont des descriptions de situations, à des états représentant des définitions générales d'un ensemble d'états. Ces définitions communes permettent la création d'ensembles d'états sous différents niveaux d'abstraction et de conceptualisation. Enfin, un processus de décompositions des trajectoires est réalisé afin de résoudre un problème grâce à la résolution de ses sous-problèmes associés. Cette décomposition facilite l'adaptation des trajectoires et l'estimation des résultats des transformations. Basé sur cette méthode, un outil a été développé en programmation logique, sous Prolog. La modélisation et l'évaluation des voies de valorisation se fait grâce à la création d'outil spécifique. Cet outil utilise la méta-programmation permettant la réalisation dynamique de modèle de structure. Le comportement de ces structures est régi par la définition de contraintes sur les différents flux circulants dans l'ensemble de la trajectoire. Lors de la modélisation de la trajectoire, ces contraintes sont converties par un parser permettant la réalisation d'un modèle de programmation par contraintes cohérent. Ce dernier peut ensuite être résolu grâce à des solveurs via une interface développée et intégrée au système. De même, plusieurs greffons ont été réalisés pour analyser et évaluer les trajectoires à l'aide des critères retenus. / In this work, a study is realised about the creation of a new methodology allowing the generation and the assessment of new waste recovery processes. Three elements are proposed for that. The first one is the creation of a modelling framework permitting a structured and homogeneous representation of each recovery process and the criteria used to asses them. The second one is a system and a tool generating new recovery processes from others known. Finally, the last element is another tool to model, to estimate and to asses the generated processes. The creation of a modelling framework tries to create some categories of elements allowing the structuring of unit operations under different levels of description. Three levels have been identified. In the higher level, the Generic operation which describes global structure of operations. The second one is Generic treatment which is an intermediate level between the two others. It proposes here too categories of operations but more detailed than the higher level. The last one is the Unit operation. A second framework has been created. It is more conceptual and it has two components : blocs and systems. These frameworks are used with a set of selected indicators. In a desire of integrating our work in a sustainable development approach, an indicator has been chosen for each of its components: economical, environmental and social. In our study, the social impact is limited to the number of created jobs. To estimate this indicator, we proposed a new method based on economical values of a company. The tool for the generation of new waste recovery processes used the methodology of case-based reasoning CBR which is based on the knowledge management. Some difficult points are treated here to adapt the CBR to our problem. The structuring of knowledge and generally the source case generation is realised by a system based on connections between data and the use of inference mechanisms. The development of a new method for the similarity measure is designed with the introduction of common definition concept which allows linking states, simply put description of objects, to other states under different levels of conceptualizations and abstractions. This point permits creating many levels of description. Finally, recovery process is decomposed from a main problem to some sub-problems. This decomposition is a part of the adaptation mechanism of the selected source case. The realisation of this system is under logic programming with Prolog. This last one permits the use of rules allowing inferences and the backtracking system allowing the exploration to the different possible solution. The modelling and assessment of recovery processes are done by a tool programmed in Python. It uses the meta-programming to dynamically create model of operations or systems. Constraint rules define the behaviour of these models allowing controlling the flux circulating in each one. In the evaluation step, a parser is used to convert theses rules into a homogeneous system of constraint programming. This system can be solved by the use of solvers with an interface developed for that and added to the tool. Therefore, it is possible for the user to add solvers but also to add plug-ins. This plug-ins can make the assessment of the activity allowing to have different kinds of evaluation for the same criteria. Three plug-ins are developed, one for each selected criterion. These two methods are tested to permit the evaluation of the proposed model and to check the behaviour of them and their limits . For these tests, a case-base on waste has been created Finally, for the modelling and assessment tool, a study case about the recovery process of used tyres in new raw material is done.