Cette thèse propose un modèle permettant la mise en œuvre d'un système de raisonnement à partir de cas capable d'adapter des procédures représentées sous forme de texte en langue naturelle, en réponse à des requêtes d'utilisateurs. Bien que les cas et les solutions soient sous forme textuelle, l'adaptation elle-même est d'abord appliquée à un réseau de contraintes temporelles exprimées à l'aide d'une algèbre qualitative, grâce à l'utilisation d'un opérateur de révision des croyances. Des méthodes de traitement automatique des langues sont utilisées pour acquérir les représentations algébriques des cas ainsi que pour regénérer le texte à partir du résultat de l'adaptation / This thesis proposes a practical model making it possible to implement a case-based reasoning system that adapts processes represented as natural language text in response to user queries. While the cases and the solutions are in textual form, the adaptation itself is performed on networks of temporal constraints expressed with a qualitative algebra, using a belief revision operator. Natural language processing methods are used to acquire case representations and to regenerate text based on the adaptation result

Made available in DSpace on 2016-12-23T14:33:36Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 2123194 bytes, checksum: b9d4a5d39eaf88559d7055b13547ab8e (MD5) Previous issue date: 2006-11-27 / Due to the complexity and the high number of variables involved in the management activities, it is essential to the project manager to have some kind of automated support to perform her tasks. During the accomplishment of a software project, a high amount of knowledge is produced and used. Looking for the reuse of that knowledge in future projects, we need to provide means to retain and store the generated knowledge in a way to minimize the effort to obtain it in the future. In this context, knowledge management can be used to capture the knowledge and experience generated and accumulated during the software process and to promote the appearance of new knowledge. Experience constitutes a key factor in order to management activities can be accomplished with success. Thus, the benefits reached by the change of ideas during the accomplishment of those activities are evident. During this work, in order to support software project management using knowledge management in the software development environment ODE, we have developed and integrated to ODE an infrastructure to support software items characterization and search for similar items and an infrastructure to support group decision. To evaluate the potential of these infrastructures, we specialized them, respectively, to support project characterization and cooperative elaboration of risk plans. / Tendo em vista a complexidade das atividades de gerência e a quantidade de variáveis envolvidas nessas atividades, é essencial que o gerente de projetos conte com algum tipo de apoio automatizado para realizá-las. Durante a realização de um projeto de software, muito conhecimento é produzido e utilizado. Visando à reutilização desse conhecimento em projetos futuros, é fundamental que sejam providos meios de se reter e armazenar o conhecimento gerado, de forma a minimizar o esforço para obtê-lo no futuro. Neste contexto, a gerência de conhecimento pode ser usada para capturar o conhecimento e a experiência gerada e acumulada durante o processo de software e promover o surgimento de novo conhecimento. A experiência constitui um fator de fundamental importância para que as atividades de gerência sejam realizadas com sucesso. Assim, os benefícios alcançados pela troca de idéias durante a realização dessas atividades são evidentes. Durante este trabalho, visando ao apoio de gerência de conhecimento à gerência de projetos de software no ambiente de desenvolvimento de software ODE, foram desenvolvidas e integradas a ODE uma infra-estrutura para caracterização de itens de software e busca de itens similares e uma infra-estrutura de apoio à decisão em grupo. Para avaliar o potencial dessas infra-estruturas, foram conduzidas especializações das mesmas, respectivamente, para caracterização de projetos e para a elaboração cooperativa de planos de riscos.

Made available in DSpace on 2016-12-23T14:33:37Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 8391728 bytes, checksum: 9bece3ebe6f8b8e7853c4336a5dd29f5 (MD5) Previous issue date: 2006-09-10 / Tendo em vista a complexidade das atividades de gerência e a quantidade de variáveis envolvidas nessas atividades, é essencial que o gerente de projetos conte com algum tipo de apoio automatizado para realizá-las. Durante a realização de um projeto de software, muito conhecimento é produzido e utilizado. Visando à reutilização desse conhecimento em projetos futuros, é fundamental que sejam providos meios de se reter e armazenar o conhecimento gerado, de forma a minimizar o esforço para obtê-lo no futuro. Neste contexto, a gerência de conhecimento pode ser usada para capturar o conhecimento e a experiência gerada e acumulada durante o processo de software e promover o surgimento de novo conhecimento. A experiência constitui um fator de fundamental importância para que as atividades de gerência sejam realizadas com sucesso. Assim, os benefícios alcançados pela troca de idéias durante a realização dessas atividades são evidentes. Durante este trabalho, visando ao apoio de gerência de conhecimento à gerência de projetos de software no ambiente de desenvolvimento de software ODE, foram desenvolvidas e integradas a ODE uma infra-estrutura para caracterização de itens de software e busca de itens similares e uma infra-estrutura de apoio à decisão em grupo. Para avaliar o potencial dessas infra-estruturas, foram conduzidas especializações das mesmas, respectivamente, para caracterização de projetos e para a elaboração cooperativa de planos de riscos. / Due to the complexity and the high number of variables involved in the management activities, it is essential to the project manager to have some kind of automated support to perform her tasks. During the accomplishment of a software project, a high amount of knowledge is produced and used. Looking for the reuse of that knowledge in future projects, we need to provide means to retain and store the generated knowledge in a way to minimize the effort to obtain it in the future. In this context, knowledge management can be used to capture the knowledge and experience generated and accumulated during the software process and to promote the appearance of new knowledge. Experience constitutes a key factor in order to management activities can be accomplished with success. Thus, the benefits reached by the change of ideas during the accomplishment of those activities are evident. During this work, in order to support software project management using knowledge management in the software development environment ODE, we have developed and integrated to ODE an infrastructure to support software items characterization and search for similar items and an infrastructure to support group decision. To evaluate the potential of these infrastructures, we specialized them, respectively, to support project characterization and cooperative elaboration of risk plans.

L'expérience joue un rôle crucial dans la résolution de problèmes. Dans les activités inventives de résolution de problèmes, l’expérience est composée de deux parties : l’une est le savoir-faire spécifique acquis dans la pratique de la résolution de problèmes passés, l’autre est la connaissance supplémentaire provenant d’autres domaines dans lesquels la résolution de problèmes a déjà été acquise et est utilisée pour résoudre. Cette thèse propose une nouvelle façon de résoudre des problèmes d’invention en capitalisant l’expérience tirée d’activités de résolution de problèmes antérieures. La première contribution est basée sur l'utilisation du raisonnement à partir de cas pour collecter et accéder rapidement aux expériences. La deuxième contribution consiste à proposer une nouvelle façon de classer les effets physiques basé sur l'utilisation de Wikipédia. Pour mettre en œuvre l'approche proposée dans la thèse, une application Web appelée CBRID (Raisonnement à partir de cas pour la Conception Inventive) est développée. Par ailleurs, nous avons mené une série d’expériences pour évaluer notre approche en termes d’efficacité et d’efficience. / Experience plays a crucial role in the resolution of problems. When in inventive problem solving activities, experience is composed of two parts: one is the specific know-how knowledge acquired in the practice of solving previous problems, the other is the additional knowledge from other domains where the problem solver is previously acquired and is used for problem solving. This thesis aims at proposing a new way to solve new inventive problems by capitalizing experience obtained from past problem solving activities. The first contribution is based on the use of the case-based reasoning for collecting and rapidly accessing the experiences. The second contribution consists in proposing a new way to classify the physical effects using Wikipedia. To implement the proposed approach, a web-based application called CBRID (Case-based reasoning for Inventive Design) is developed. A particular case of ''cloth hanger'' is studied to illustrate the problem solving process based on the proposed approach. In addition to that, we conducted a set of experiments to evaluate our approach in terms of effectiveness and efficiency.

La combinaison du traitement sémantique des connaissances (Semantic Processing of Knowledge) et de la modélisation des étapes de raisonnement (Modeling Steps of Reasoning), utilisés dans le domaine clinique, offrent des possibilités intéressantes, nécessaires aussi, pour l’élaboration des ontologies médicales, utiles à l'exercice de cette profession. Dans ce cadre, l'interrogation de banques de données médicales multiples, comme MEDLINE, PubMed… constitue un outil précieux mais insuffisant car elle ne permet pas d'acquérir des connaissances facilement utilisables lors d’une démarche clinique. En effet, l'abondance de citations inappropriées constitue du bruit et requiert un tri fastidieux, incompatible avec une pratique efficace de la médecine.Dans un processus itératif, l'objectif est de construire, de façon aussi automatisée possible, des bases de connaissances médicales réutilisables, fondées sur des ontologies et, dans cette thèse, nous développons une série d'outils d'acquisition de connaissances qui combinent des opérateurs d'analyse linguistique et de modélisation de la clinique, fondés sur une typologie des connaissances mises en œuvre, et sur une implémentation des différents modes de raisonnement employés. La connaissance ne se résume pas à des informations issues de bases de données ; elle s’organise grâce à des opérateurs cognitifs de raisonnement qui permettent de la rendre opérationnelle dans le contexte intéressant le praticien.Un système multi-agents d’aide à la décision clinique (SMAAD) permettra la coopération et l'intégration des différents modules entrant dans l'élaboration d'une ontologie médicale et les sources de données sont les banques médicales, comme MEDLINE, et des citations extraites par PubMed ; les concepts et le vocabulaire proviennent de l'Unified Medical Language System (UMLS).Concernant le champ des bases de connaissances produites, la recherche concerne l'ensemble de la démarche clinique : le diagnostic, le pronostic, le traitement, le suivi thérapeutique de différentes pathologies, dans un domaine médical donné.Différentes approches et travaux sont recensés, dans l’état de question, et divers paradigmes sont explorés : 1) l'Evidence Base Medicine (une médecine fondée sur des indices). Un indice peut se définir comme un signe lié à son mode de mise en œuvre ; 2) Le raisonnement à partir de cas (RàPC) se fonde sur l'analogie de situations cliniques déjà rencontrées ; 3) Différentes approches sémantiques permettent d'implémenter les ontologies.Sur l’ensemble, nous avons travaillé les aspects logiques liés aux opérateurs cognitifs de raisonnement utilisés et nous avons organisé la coopération et l'intégration des connaissances exploitées durant les différentes étapes du processus clinique (diagnostic, pronostic, traitement, suivi thérapeutique). Cette intégration s’appuie sur un SMAAD : système multi-agent d'aide à la décision. / The combination of semantic processing of knowledge and modelling steps of reasoning employed in the clinical field offers exciting and necessary opportunities to develop ontologies relevant to the practice of medicine. In this context, multiple medical databases such as MEDLINE, PubMed are valuable tools but not sufficient because they cannot acquire the usable knowledge easily in a clinical approach. Indeed, abundance of inappropriate quotations constitutes the noise and requires a tedious sort incompatible with the practice of medicine.In an iterative process, the objective is to build an approach as automated as possible, the reusable medical knowledge bases is founded on an ontology of the concerned fields. In this thesis, the author will develop a series of tools for knowledge acquisition combining the linguistic analysis operators and clinical modelling based on the implemented knowledge typology and an implementation of different forms of employed reasoning. Knowledge is not limited to the information from data, but also and especially on the cognitive operators of reasoning for making them operational in the context relevant to the practitioner.A multi-agent system enables the integration and cooperation of the various modules used in the development of a medical ontology.The data sources are from medical databases such as MEDLINE, the citations retrieved by PubMed, and the concepts and vocabulary from the Unified Medical Language System (UMLS).Regarding the scope of produced knowledge bases, the research concerns the entire clinical process: diagnosis, prognosis, treatment, and therapeutic monitoring of various diseases in a given medical field.It is essential to identify the different approaches and the works already done.Different paradigms will be explored: 1) Evidence Based Medicine. An index can be defined as a sign related to its mode of implementation; 2) Case-based reasoning, which based on the analogy of clinical situations already encountered; 3) The different semantic approaches which are used to implement ontologies.On the whole, we worked on logical aspects related to cognitive operators of used reasoning, and we organized the cooperation and integration of exploited knowledge during the various stages of the clinical process (diagnosis, prognosis, treatment, therapeutic monitoring). This integration is based on a SMAAD: multi-agent system for decision support.

Afin d'accompagner les professionnels de santé dans leur démarche clinique, plusieurs systèmes de suivi et deprise en charge médicale ont été construits et déployés dans le milieu hospitalier. Ces systèmes permettentprincipalement de collecter des données médicales sur les patients, de les analyser et de présenter les résultats dedifférentes manières. Ils représentent un appui et une aide aux professionnels de santé dans leur prise de décisionpar rapport à l'évolution de l'état de santé des patients suivis. L'utilisation de tels systèmes nécessitesystématiquement une adaptation à la fois au domaine médical concerné et au mode d'intervention. Il estnécessaire, dans un milieu hospitalier, que ces systèmes puissent s'adapter et évoluer d'une manière simple, enlimitant toute maintenance corrective ou évolutive. Ils doivent être en mesure de prendre en compte dynamiquementdes connaissances théoriques et empiriques du domaine issues des experts médicaux.Afin de répondre à ces exigences, nous avons proposé une approche pour la construction d'un système d'aide à ladécision médicale capable de s'adapter au domaine médical concerné et au mode d'intervention approprié pourassister les professionnels de santé dans leur démarche clinique. Cette approche permet notamment l'organisationde la collecte des données médicales, en tenant compte du contexte du patient, la représentation desconnaissances du domaine à base d'ontologies ainsi que leur exploitation associée aux guides de bonnes pratiqueset à l'expérience clinique.Dans la continuité des travaux précédemment réalisés au sein de notre équipe de recherche, nous avons choisid'enrichir, avec notre approche, la plateforme E-care qui est dédiée au suivi et à la détection précoce de touteanomalie de l'état de patients atteints de maladies chroniques. Nous avons pu ainsi adapter facilement la plateformeE-care aux différentes expérimentations qui sont été menées notamment dans des EPHAD de la MutualitéFrançaise en Anjou-Mayenne, au CHU de Hautepierre et au CHUV à Lausanne.Les résultats de ces expérimentations ont montré l'efficacité de l'approche proposée. L'adaptation de la plateformepar rapport au domaine et au mode d'intervention de chacune de ces expérimentations se limite à de la simpleconfiguration. De plus, l'approche proposée a suscité l'intérêt du personnel médical par rapport à l'organisation de lacollecte des données, qui tient compte du contexte du patient, et par rapport à l'exploitation des connaissancesmédicales qui apporte aux professionnels de santé une assistance pour une meilleure prise de décision. / To support health professionals in their clinical processes, several monitoring and medical care systems have beenbuilt and deployed in the hospital setting. These systems are mainly used to collect medical data on patients,analyze and present the outcomes in different ways. They represent support and assistance to health professionalsin their decision making regarding the evolution in the health status of the patients followed. The use of suchsystems always requires an adaptation to both the medical field and the mode of intervention. It is necessary, in ahospital setting, to adapt and evolve these systems in a simple manner, limiting any corrective or evolutionarymaintenance. Moreover, these systems should be able to consider dynamically the domain knowledge from medicalexperts.To meet these requirements, we proposed an approach for the construction of a medical decision support system(MDSS). This MDSS can adapt to the medical field and to the appropriate mode of intervention to assist healthprofessionals in their clinical processes. This approach allows especially the organization of the medical datacollection by taking into account the patient¿s context, the ontology-based knowledge representation of the domainand permits the exploitation of the medical guidelines and the clinical experience.In continuity of our research team¿s previous work, we chose to expand with our approach, the E-care platformwhich is dedicated to monitoring and early detection of any abnormality of the health status of patients with chronicdiseases. We were able to adapt easily the E-care platform for the various experiments that have been conducted,including EPHAD of the Mutualité Française in Anjou-Mayenne, Hautepierre hospital and Lausanne hospital(CHUV).The outcomes of these experiments have shown the effectiveness of the proposed approach. Where, the adaptationof the platform regarding to the domain and mode of intervention of each of these experiments is limited to thesimple configuration. Furthermore, the proposed approach has attracted the interest of the medical staff regardingthe organization of the medical data collection, and the exploitation of the medical knowledge which bringsassistance to the health professionals for better decision making.

In Multi-Agenten-Systemen (MAS) kooperieren und konkurrieren Agenten um ihre jeweiligen Ziele zu erreichen. Für optimierte Agenten-Interaktionen sind Kenntnisse über die aktuellen und zukünftigen Handlungen anderer Agenten (Interaktionsparter, IP) hilfreich. Bei der Ermittlung und Nutzung solcher Kenntnisse kommt dem automatischen Erkennen und Verstehen sowie der Vorhersage von Verhalten der IP auf Basis von Beobachtungen besondere Bedeutung zu. Die Dissertation beschäftigt sich mit der automatischen Bestimmung und Vorhersage von Verhalten der IP durch einen Modellierenden Agenten (MA). Der MA generiert fallbasierte, adaptive Verhaltens-Modelle seiner IP und verwendet diese zur Vorhersage ihrer Verhalten. Als Anwendungsszenario wird mit dem virtuellen Fußballspiel des RoboCup ein komplexes und populäres MAS betrachtet. Der Hauptbeitrag dieser Arbeit besteht in der Ausarbeitung, Realisierung und Evaluierung eines Ansatzes zur automatischen Verhaltens-Modellierung für ein komplexes Multi-Agenten-System. / In multi-agent-systems agents cooperate and compete to reach their personal goals. For optimized agent interactions it is helpful for an agent to have knowledge about the current and future behavior of other agents. Ideally the recognition and prediction of behavior should be done automatically. This work addresses a way of automatically classifying and an attempt at predicting the behavior of a team of agents, based on external observation only. A set of conditions is used to distinguish behaviors and to partition the resulting behavior space. From observed behavior, team specific behavior models are then generated using Case Based Reasoning. These models, which are derived from a number of virtual soccer games (RoboCup), are used to predict the behavior of a team during a new game. The main contribution of this work is the design, realization and evaluation of an automatic behavior modeling approach for complex multi-agent systems.

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.