L'activité de conception est un processus complexe et décisif dans le cycle de vie des produits et des procédés de fabrication. Dans le contexte actuel, les chercheurs et ingénieurs de conception notent une nette augmentation de la complexité des produits et procédés, pour satisfaire au mieux l’ensemble des exigences croissantes provenant de l’ensemble des acteurs du cycle de vie (industriels et utilisateurs) mais aussi du monde normatif. La gestion des connaissances et de l’expertise métier est un atout important pour rendre plus efficace et accélérer ce processus. Les recherches actuelles sur la gestion des connaissances font émerger des méthodes et outils performants pour identifier, formaliser, exploiter et diffuser la connaissance et les expériences issues de conceptions passées en vue de produire rapidement de nouvelles solutions. Parmi les approches existantes le Raisonnement à Partir de Cas (RàPC) et la Programmation Par Contraintes (PPC) correspondent aux besoins identifiés en Génie des Procédés. A partir de l’analyse de ces deux approches, ce travail propose un couplage du RàPC et de la PPC afin de fournir un cadre méthodologique et un outil logiciel pour une aide à la conception. Le RàPC permet de capitaliser et de remémorer les expériences passées. Toutefois, la modification de la solution passée pour répondre aux exigences du nouveau problème nécessite l’ajout de nouvelles connaissances aussi appelées connaissances d’adaptation. La PPC, quant à elle, offre justement un cadre approprié pour modéliser et gérer la connaissance permettant l’obtention d’une solution à un problème mais aussi ces connaissances d’adaptation. Outre la formalisation des connaissances d’adaptation, une des difficultés réside dans l’acquisition de ces connaissances. Dans l’approche proposée, le cycle traditionnel du RàPC a été modifié de façon à créer une boucle d’interaction avec l’utilisateur. Lorsqu’un échec d’adaptation se produit, cette boucle est activée et l’expert est sollicité pour apporter les modifications nécessaires à l’obtention d’une solution appropriée. Cette correction est l’occasion d’acquérir en ligne cette nouvelle connaissance, qui sera par la suite mise à jour et ajoutée dans le système. Un cas d’étude sur la conception d’une opération unitaire de génie des procédés permet d’illustrer l’approche. / Design is a complex and crucial process within the lifecycle of products and production processes. In the current context, design engineers and researchers notice an increasing in complexity of products and processes, in order to meet all the requirements coming from all the participants(manufacturers and users alike) in the life cycle and in the normative world as well. Knowledge management is an important asset to accelerate this process and improve its efficiency. Current research on knowledge management is producing new methods and tools to identify, formalize, exploit and disseminate knowledge from past designs experiences to produce new solutions rapidly. Among existing approaches, Case-Based Reasoning (CBR) and Constraint Programming (CP) are suited to needs identified in Process Engineering. Based on the analysis of these two approaches, this work proposes a coupling of CBR and the CP to provide a methodological framework and a software tool to assist design. The CBR allows to capitalize and retrieve past experiences. However, transforming the past solution to fit the new problem requirements needs the addition of new knowledge also known as Adaptation Knowledge. CP, meanwhile, offers an appropriate framework to model and manage knowledge required to obtain an appropriate solution to a problem, but also the adaptation knowledge. In addition to the formalization of adaptation knowledge, one of the remaining major difficulties lies in knowledge acquisition. In the proposed approach, the traditional CBR cycle has been modified to create a user interaction loop. When an adaptation failure occurs, this loop is activated and the expert is asked to make the necessary changes to achieve an appropriate solution. This correction is an opportunity to acquire this new knowledge online, which will be subsequently updated and added into the system. A case study on the design of a unit operation of Process Engineering is used to illustrate the approach

Erfahrungsmanagement (EM) ist eine Spezialform des Wissensmanagements, die sich mit aufgabenbezogenem Wissen beschäftigt. Diese Arbeit entwickelt ein Rahmenwerk für Assistenzsysteme, die Menschen bei EM-Aufgaben unterstützen. Es untersucht nicht nur technische Fragen (Erfahrungswissen sammeln, strukturieren, speichern und wiederverwenden) sondern auch organisatorische (Erfahrungswissen evaluieren und pflegen) und psychosoziale Aspekte (ein EM-System integrieren, Barrieren vermeiden, den Systemeinsatz bewerten). Fallbasierte Anwendungsbeispiele für industrielle und experimentelle Szenarien zeigen, welche Prozesse wo unterstützt oder gar teilautomatisiert werden können. Sie dienen der experimentellen Evaluierug der Fragen, die ich zu Beginn jedes Anwendungskapitels formuliert habe. / Experience Management (EM) is a special form of Knowledge Management that deals with task-based knowledge. This thesis provides a framework for assistant systems that support human beings in EM tasks. It deals not only with technical issues (how to collect, structure, store, retrieve, and reuse experiential knowledge), but als with organizational issues (how to evaluate and maintain it) and psychosocial questions (how to integrate an EM system, how to avoid barriers, how to evaluate the success of the whole system). Case-based sample applications from both, industrial and experimental scenarios, show to what extend the particular EM processes can be supported or which sub-processes can even be automated. By means of experiments with these implemented samples, we evaluate the topics that are discussed at the beginning of each application chapter.

Mesmo com a evolução tecnológica em vários setores, a área de enfermagem tem tido investimentos escassos em pesquisa e desenvolvimento capazes de atender suas expectativas, principalmente no campo da inteligência artificial. As expectativas dos enfermeiros convergem à melhora de seus processos clínicos que resultará em uma maior aproximação de seus pacientes. Além disso, há dificuldade em reunir diagnósticos de enfermagem nos hospitais, onde diversos registros clínicos e procedimentos preenchidos manualmente e armazenados ainda em folhas de papel. Esta condição compromete a legibilidade dos documentos envolvidos nos processos hospitalares, e seu arquivamento torna o processo de levantamento de informações moroso, o que acaba por inviabilizar a pesquisa à qual poderia resultar em informações importantes para melhora do processo de tomada de decisões. O objetivo desta dissertação foi trazer o estado da arte em inteligência artificial focado em raciocínio baseado em casos e sua aplicação na sistematização da assistência de enfermagem. No sentido de validar o modelo levantado foi criado um protótipo para apresentar uma aplicação que pudesse auxiliar os enfermeiros em seus processos clínicos, armazenando suas experiências em uma base de casos para futuras pesquisas. O protótipo consistiu em digitalizar diagnósticos de enfermagem pediátrica, e inserção em uma base de casos, com o intuito de avaliar a eficácia do protótipo na manipulação destes casos, em uma estrutura propicia para recuperação, adaptação, indexação e comparação de casos. Esta dissertação apresenta como resultado uma ferramenta computacional para a área da saúde, empregando uma das técnicas de inteligência artificial, Raciocínio Baseados em Casos. Os resultados foram satisfatórios devido ao alto índice de aprovação nos quesitos confiabilidade, funcionalidade, usabilidade e eficiência conforme as normas ISO/ABNT de qualidade em software. / Even with the development of technology in many industries, the nursing sector has had low investment in research and development, mainly in the field of artificial intelligence. The expectations of nurses converge to improvement over his clinical procedures that will result in a closer relationship with their patients. Moreover, there is difficulty in finding nursing diagnoses in hospitals, while clinical records and procedures are completed manually and stored even on paper. This condition compromises the readability of documents involved in the admissions process, and archiving that also becomes time-consuming process of information gathering, which derail research that could result in important information for improved decision-making. The objective of this dissertation was to bring the state of the art regarding artificial intelligence focusing on Case-Based Reasoning and its application in the systematization of nursing care. In order to validate the model a prototype was set up to demonstrate an application that would assist nurses in their clinical files, storing their experiences in a case base for future research. The prototype was to scan diagnosis pediatric nursing, and insertion into a case base in order to evaluate the effectiveness of the prototype in handling those cases. It also provides a framework for recovery, adaptation, indexing, and comparison of cases. This dissertation presents results in a computational tool for health employing one of the techniques of artificial intelligence: Case-Based Reasoning. The results were satisfactory due to high rate in terms of structure reliability, functionality, usability and efficiency according to ISO / ABNT quality in software.

Mesmo com a evolução tecnológica em vários setores, a área de enfermagem tem tido investimentos escassos em pesquisa e desenvolvimento capazes de atender suas expectativas, principalmente no campo da inteligência artificial. As expectativas dos enfermeiros convergem à melhora de seus processos clínicos que resultará em uma maior aproximação de seus pacientes. Além disso, há dificuldade em reunir diagnósticos de enfermagem nos hospitais, onde diversos registros clínicos e procedimentos preenchidos manualmente e armazenados ainda em folhas de papel. Esta condição compromete a legibilidade dos documentos envolvidos nos processos hospitalares, e seu arquivamento torna o processo de levantamento de informações moroso, o que acaba por inviabilizar a pesquisa à qual poderia resultar em informações importantes para melhora do processo de tomada de decisões. O objetivo desta dissertação foi trazer o estado da arte em inteligência artificial focado em raciocínio baseado em casos e sua aplicação na sistematização da assistência de enfermagem. No sentido de validar o modelo levantado foi criado um protótipo para apresentar uma aplicação que pudesse auxiliar os enfermeiros em seus processos clínicos, armazenando suas experiências em uma base de casos para futuras pesquisas. O protótipo consistiu em digitalizar diagnósticos de enfermagem pediátrica, e inserção em uma base de casos, com o intuito de avaliar a eficácia do protótipo na manipulação destes casos, em uma estrutura propicia para recuperação, adaptação, indexação e comparação de casos. Esta dissertação apresenta como resultado uma ferramenta computacional para a área da saúde, empregando uma das técnicas de inteligência artificial, Raciocínio Baseados em Casos. Os resultados foram satisfatórios devido ao alto índice de aprovação nos quesitos confiabilidade, funcionalidade, usabilidade e eficiência conforme as normas ISO/ABNT de qualidade em software. / Even with the development of technology in many industries, the nursing sector has had low investment in research and development, mainly in the field of artificial intelligence. The expectations of nurses converge to improvement over his clinical procedures that will result in a closer relationship with their patients. Moreover, there is difficulty in finding nursing diagnoses in hospitals, while clinical records and procedures are completed manually and stored even on paper. This condition compromises the readability of documents involved in the admissions process, and archiving that also becomes time-consuming process of information gathering, which derail research that could result in important information for improved decision-making. The objective of this dissertation was to bring the state of the art regarding artificial intelligence focusing on Case-Based Reasoning and its application in the systematization of nursing care. In order to validate the model a prototype was set up to demonstrate an application that would assist nurses in their clinical files, storing their experiences in a case base for future research. The prototype was to scan diagnosis pediatric nursing, and insertion into a case base in order to evaluate the effectiveness of the prototype in handling those cases. It also provides a framework for recovery, adaptation, indexing, and comparison of cases. This dissertation presents results in a computational tool for health employing one of the techniques of artificial intelligence: Case-Based Reasoning. The results were satisfactory due to high rate in terms of structure reliability, functionality, usability and efficiency according to ISO / ABNT quality in software.

Made available in DSpace on 2016-04-29T14:23:07Z (GMT). No. of bitstreams: 1 Allex Motta Melo da Rocha.pdf: 4303012 bytes, checksum: 94163da5edc7a5c0777f5f42582edc52 (MD5) Previous issue date: 2012-05-24 / This current dissertation aims to use the techniques of Case-Based Reasoning (CBR) and Geographic Information System (GIS) together to identify and characterize the impact of a contagious injury in the city of Belém, Pará state, Brazil, caused by the bacterium Leptospira interrogans, known as leptospirosis. Environmental and health issues, particularly as the presence of rodent droppings, urine and feces, influencing an establishment of this disease. This research conducts a study of the use of CBR technique in order to formulate and develop a model to identify and assist in the diagnosis of the disease, using data collected from patients, such as epidemiological history and clinical, in the area, in the years 2008, 2009 and 2010. The CBR system will enable health professionals can use it as a support tool in the diagnosis of cases of the disease. This dissertation also applies GIS techniques to visually express and produce environmental analysis, temporal and socioeconomic of leptospirosis epidemiology scenario in a neighborhood in the city of Belém, in the years mentioned above, due to yours epidemiological importance. So, there will be the environmental and socioeconomic characteristics, in order to analyze the spatial distribution of the main reservoirs of this disease and to identify the habits of life of people living or working in these places, considering the environmental factor to provide subsidies to adopt preventive measures to control of disease incidence / A presente dissertação tem como objetivo utilizar as técnicas de Raciocínio Baseado em Casos (RBC) e Geoprocessamento, em conjunto, para identificar e caracterizar a incidência de um agravo infectocontagioso, na cidade de Belém, estado do Pará, Brasil, ocasionado pela bactéria Leptospira interrogans, conhecido como leptospirose humana. Questões ambientais e sanitárias, como a presença, principalmente, de excrementos de roedores, como urina e fezes influenciam o estabelecimento deste agravo. Esta pesquisa realiza um estudo do uso da técnica de RBC, com o objetivo de formular e desenvolver um modelo para identificar e auxiliar no diagnóstico da doença, por meio de dados coletados dos pacientes, tais como antecedentes epidemiológicos e clínicos, ocorridos na região, nos anos de 2008, 2009 e 2010. O sistema de RBC possibilitará que profissionais da saúde possam utilizá-lo como ferramenta de apoio na diagnose de casos da doença. Esta dissertação também aplica técnicas de Geoprocessamento para expressar visualmente e produzir análises ambientais, temporais e socioeconômicas do cenário epidemiológico da leptospirose em um bairro na cidade de Belém, nos anos citados anteriormente, devido à importância epidemiológica do mesmo. Para tal, será realizada a caracterização ambiental e socioeconômica, a fim de analisar a distribuição espacial dos principais reservatórios deste agravo e identificar os hábitos de vida das populações que moram ou trabalham nestes locais, considerando o fator ambiental para fornecer subsídios à adoção de medidas preventivas para o controle da incidência desta doença

With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many of the tools and algorithms that drive development and applications. While we have already achieved many successes in the fields of image recognition, language processing, recommendation engines, robotics, or autonomous systems, most progress was achieved when the algorithms were focused on learning only a single task with little regard to effort and reusability. Since learning a new task from scratch often involves an expensive learning process, in this work, we are considering the use of previously acquired knowledge to speed up the learning of a new task. For that, we investigated the application of Transfer Learning methods for Deep Reinforcement Learning (DRL) agents and propose a novel framework for knowledge preservation and reuse. We show, that the knowledge transfer can make a big difference if the source knowledge is chosen carefully in a systematic approach. To get to this point, we provide an overview of existing literature of methods that realize knowledge transfer for DRL, a field which has been starting to appear frequently in the relevant literature only in the last two years. We then formulate the Case-based Reasoning methodology, which describes a framework for knowledge reuse in general terms, in Reinforcement Learning terminology to facilitate the adaption and communication between the respective communities. Building on this framework, we propose Deep Case-based Policy Inference (DECAF) and demonstrate in an experimental evaluation the usefulness of our approach for sequential task learning with knowledge preservation and reuse. Our results highlight the benefits of knowledge transfer while also making aware of the challenges that come with it. We consider the work in this area as an important step towards more stable general learning agents that are capable of dealing with the most complex tasks, which would be a key achievement towards Artificial General Intelligence. / Com a evolução da Aprendizagem Profunda (Deep Learning), o campo da Inteligência Artificial (IA) entrou em uma nova era. Juntamente com uma quantidade crescente de dados e recursos computacionais cada vez mais aprimorados, o Aprendizado de Máquina estabelece a base para a IA moderna, fornecendo muitas das ferramentas e algoritmos que impulsionam seu desenvolvimento e aplicações. Apesar dos muitos sucessos nas áreas de reconhecimento de imagem, processamento de linguagem natural, sistemas de recomendação, robótica e sistemas autônomos, a maioria dos avanços foram feitos focando no aprendizado de apenas uma única tarefa, sem muita atenção aos esforços dispendidos e reusabilidade da solução. Como o aprendizado de uma nova tarefa geralmente envolve um processo de aprendizado despendioso, neste trabalho, estamos considerando o reúso de conhecimento para acelerar o aprendizado de uma nova tarefa. Para tanto, investigamos a aplicação dos métodos de Transferência de Aprendizado (Transfer Learning) para agentes de Aprendizado por Reforço profundo (Deep Reinforcement Learning - DRL) e propomos um novo arcabouço para preservação e reutilização de conhecimento. Mostramos que a transferência de conhecimento pode fazer uma grande diferença no aprendizado se a origem do conhecimento for escolhida cuidadosa e sistematicamente. Para chegar a este ponto, nós fornecemos uma visão geral da literatura existente de métodos que realizam a transferência de conhecimento para DRL, um campo que tem despontado com frequência na literatura relevante apenas nos últimos dois anos. Em seguida, formulamos a metodologia Raciocínio baseado em Casos (Case-based Reasoning), que descreve uma estrutura para reutilização do conhecimento em termos gerais, na terminologia de Aprendizado por Reforço, para facilitar a adaptação e a comunicação entre as respectivas comunidades. Com base nessa metodologia, propomos Deep Casebased Policy Inference (DECAF) e demonstramos, em uma avaliação experimental, a utilidade de nossa proposta para a aprendizagem sequencial de tarefas, com preservação e reutilização do conhecimento. Nossos resultados destacam os benefícios da transferência de conhecimento e, ao mesmo tempo, conscientizam os desafios que a acompanham. Consideramos o trabalho nesta área como um passo importante para agentes de aprendizagem mais estáveis, capazes de lidar com as tarefas mais complexas, o que seria um passo fundamental para a Inteligência Geral Artificial.

<p>A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions.</p><p>A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.</p> / IPOS, PROEK

Visual media are of great importance to designers. Understanding a new design, for example, often means understanding a drawing. From the perspective of artificial intelligence, this implies that automated knowledge acquisition in computer-aided design can productively occur using drawings as a knowledge source. However, this requires machines that are able to interpret design drawings. I view the task of interpreting drawings as one of constructing a teleological model of the design depicted in the drawings, where the model enables causal and functional inferences about the depicted design. I have developed a novel analogical method for constructing a teleological model of a mechanical device from an unlabelled 2D line drawing. The source case is organized in a Drawing Shape Structure Behavior Function (DSSBF) abstraction hierarchy. This knowledge organization enables the analogical mapping and transfer to occur at multiple levels of abstraction. Given a target drawing and a relevant source case, my method of compositional analogy first constructs a graphical representation of the lines and the intersections in the target drawing, then uses the mappings at the level of line intersections to transfer the shape representations from the source case to the target. It next uses the mappings at the level of shapes to transfer the structural model of the device from the source to the target. Finally, the mappings from the source to the target structural model enable the transfer of behaviors and the functional specification from source to target, completing the analogy and yielding a complete DSSBF model of the input drawing. The Archytas system implements this method of compositional analogy and evaluates it in the domain of kinematic devices such as piston and crankshaft devices, door latches, and pulley systems.

老人居家照護是近來愈趨重視的議題，過去ㄧ直以來主要著重在老人生理狀態的偵測及相關居家醫療儀器的研究，但除了生理上訊號所顯現的不適之外，尚有其它的問題困擾著老人的生活。對於在老人身上所產生的許多不適，最直接的就是反映在老人的情緒上，若是能針對老人目前所處的環境狀態分析出造成老人情緒狀態轉變的因素，將有助於提升老人的生活品質。本研究所採用的替換調適模式之案例式推理，有別於一般案例式推理的應用，一般案例式推理需要對於應用領域的知識有相當了解才能達到有效地案例調適，因此在發展案例式推理的應用時，需要經過相當長的資訊收集，而替換調適模式運用一些已經存在的案例，從中萃取出案例間的關聯性，並藉由案例的不斷累積來自動化的調適案例庫中的知識，因此將使得推理的結果更符合老人過去的生活習性，因此能針對老人的情緒狀態找出形成的因素，而找出改變情緒的形成因素之後，將有機會的進一步解決老人目前所遭遇的生活難題，最終本研究期望能藉此達成提升老人生活品質的目的。 / e-Care for aging has become an increasingly important research topic in recent years. Most research focus on the detection of Physiological state or the study of the e-Care medical devices. Nevertheless, there are still other problems tormenting an aging’s life besides physiological discomfort detected from physiological signals. For instance, it is often the case that the discomfort comes from the aging's atypical mood status. In other words, causes behind the change of the aging’s mood status would help improve the quality of the aging’s life. Accordingly, this paper presents a substitution-based case adaptation CBR to analyze the causes of effecting the change of the aging’s mood status. Substitution-based case adaptation CBR differs from general CBR in lean adaptation knowledge required. Most existing CBR systems rely on an enormous amount of built-in adaptation knowledge in the form of adaptation rules (that require a deep analysis of the domain). Substitution-based case adaptation can make use of a limited number of cases to extract the relations between the cases and reach automatic adaptation. With the accumulation of cases in the case library, the result of inference fit in line with the habit of the aging’s life would be improved based on this automatic adaptation. The contribution of our method aims at reaching the e-Care goal of improving the aging’s life quality from the mental perspective.

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