Yes / Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study. / Authors acknowledge the internal funding support received from Namal College Mianwali to complete the research work.

No / Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.

Face à l’évolution technologique rapide des produits, l’innovation incrémentale des nouveaux produits, et la mobilité du personnel le plus expérimenté, les entreprises cherchent à formaliser et à capitaliser leurs expériences et les savoir-faire des acteurs d’entreprise en vue d’une réutilisation ultérieure. Afin de répondre à cette problématique, l’approche du raisonnement à partir de cas conversationnel (RàPCC) est une réponse potentielle à la question de la capitalisation et de la réutilisation des connaissances. Notre recherche s’intéresse aux méthodes permettant de piloter le retour d’expérience (RETEX) appliqué à la résolution de problèmes techniques. Notre méthodologie pour créer un système d’aide au diagnostic des pannes est divisée en quatre phases : la description d’événements, l’élaboration de l’ensemble des solutions apportées aux pannes, la mise en place d’une aide au diagnostic grâce aux arbres de défaillances et la mise en place d’un système informatique. Afin d’extraire les connaissances tacites et les formaliser, nous avons créé des protocoles de décision dans le but d’aider l’expert à résoudre un problème industriel. Nous avons donc proposé une formulation et l’élaboration de cas conversationnels dans le domaine du diagnostic. Ces cas doivent être stockés dans une base de cas. Afin de valider notre proposition méthodologique, nous avons réalisé la phase expérimentale dans une entreprise industrielle de l’Est de la France. Nous proposons finalement une maquette informatique conçue pour l’entreprise. Cette maquette permet de réaliser un diagnostic des pannes en créant des cas dans une base de cas pour une réutilisation ultérieure / Faced with the fast technological development of products, incremental innovation of new products, and the mobility of their most experienced staff, companies are seeking to formalize and capitalize on the experiences and know-how of their personnel in order to reuse them later. To deal with these problems, the conversational case based reasoning (CCBR) approach is a potential answer to the question of capitalization and reuse of knowledge. Our research focuses on methods to manage experience feedback (EF). We are placed in the field of experience feedback applied to technical problem solving. Our methodology for creating aided failure diagnosis systems is divided into four phases: the events description, the development of all solutions to failures, the arrangement of a diagnostic aid through fault trees and setting up a computer system. We based our work on the fault tree approach in order to extract tacit knowledge and its formalization. Our objective was to create decision protocols in order to assist the expert in solving an industrial problem. Therefore, we have proposed a formulation and development of conversational cases in diagnosis. These cases must be memorised in a database of cases. To validate our proposal methodology, we have carried out the experimental phase in an industrial company in eastern France. This experiment allowed us to validate our work and highlight its advantages and limitations. Finally, we propose a computer model designed for the company. This model enables failure diagnosis by creating the case in a case base for later utilization

The ability to interpret and apply experiences, or cases (Kolodner, 1993; 1997) is a skill (Anderson, et. al, 1981; Anderson, 2000) that is key to successful learning that can be transferred (Bransford, Brown and Cocking, 1999) to new learning situations. For middle-schoolers in a project-based inquiry science classroom, interpreting and applying the experiences of experts to inform their design solutions is not always easy (Owensby and Kolodner, 2002). Interpreting and applying an expert case and then assessing the solution that results from that application are the components of a process I call case use. This work seeks to answer three questions: 1. How do small-group case use capabilities develop over time? 2. How well are students able to apply case use skills in new situations over time? 3. What difficulties do learners have as they learn case use skills and as they apply case use skills in new situations? What do these difficulties suggest about how software might further support cognitive skill development using a cognitive apprenticeship (Collins, Brown and Newman, 1989) framework? I argue that if learners in project based inquiry classrooms are able to understand, engage in, and carry out the processes involved in interpreting and applying expert cases effectively, then they will be able to do several things. They will learn those process and be able to read an expert case for understanding, glean the lessons they can learn from it, and apply those lessons to their question or challenge. Furthermore, I argue that they may also be able to transfer interpretation, application, and assessment skills to other learning situations where application of cases is appropriate.

Un nombre croissant d’industries ressentent le besoin de formaliser leurs processus d’innovation. Dans ce contexte, les outils du domaine de la qualité et les approches d’aide à la créativité provenant du "brain storming" ont déjà montré leurs limites. Afin de répondre à ces besoins, la TRIZ (Acronyme russe pour Théorie de Résolution des Problèmes Inventifs), développée par l’ingénieur russe G. S. Altshuller au milieu du 20ème siècle, propose une méthode systématique de résolution de problèmes inventifs multidomaines. Selon TRIZ, la résolution de problèmes inventifs consiste en la construction du modèle et l’utilisation des sources de connaissance de la TRIZ. Plusieurs modèles et sources de connaissances permettent la résolution de problèmes inventifs de types différents, comme les quarante Principes Inventifs pour l’élimination des contradictions techniques. Toutes ces sources se situent à des niveaux d’abstractions relativement élevés et sont, donc, indépendantes d’un domaine particulier, qui nécessitent des connaissances approfondies des domaines d’ingénierie différents. Afin de faciliter le processus de résolution de problèmes inventifs, un "Système Intelligent de Gestion de Connaissances" est développé dans cette thèse. D’une part, en intégrant les ontologies des bases de connaissance de la TRIZ, le gestionnaire propose aux utilisateurs de sources de connaissance pertinentes pour le modèle qu’ils construisent, et d’autre part, le gestionnaire a la capacité de remplir "automatiquement" les modèles associés aux autres bases de connaissance. Ces travaux de recherche visent à faciliter et automatiser le processus de résolution de problèmes inventifs. Ils sont basés sur le calcul de similarité sémantique et font usage de différentes technologies provenantes de domaine de l’Ingénierie de Connaissances (modélisation et raisonnement basés sur les ontologies, notamment). Tout d’abord, des méthodes de calcul de similarité sémantique sont proposées pour rechercher et définir les liens manquants entre les bases de connaissance de la TRIZ. Ensuite, les sources de connaissance de la TRIZ sont formalisées comme des ontologies afin de pouvoir utiliser des mécanismes d’inférence heuristique pour la recherche de solutions spécifiques. Pour résoudre des problèmes inventifs, les utilisateurs de la TRIZ choisissent dans un premier temps une base de connaissance et obtiennent une solution abstraite. Ensuite, les éléments des autres bases de connaissance similaires aux éléments sélectionnés dans la première base sont proposés sur la base de la similarité sémantique préalablement calculée. A l’aide de ces éléments et des effets physiques heuristiques, d’autres solutions conceptuelles sont obtenues par inférence sur les ontologies. Enfin, un prototype logiciel est développé. Il est basé sur cette similarité sémantique et les ontologies interviennent en support du processus de génération automatique de solutions conceptuelles. / An increasing number of industries feel the need to formalize their innovation processes. In this context, quality domain tools show their limits as well as the creativity assistance approaches derived from brainstorming. TRIZ (Theory of Inventive Problem Solving) appears to be a pertinent answer to these needs. Developed in the middle of the 20th century by G. S. Althshuller, this methodology's goal was initially to improve and facilitate the resolution of technological problems. According to TRIZ, the resolution of inventive problems consists of the construction of models and the use of the corresponding knowledge sources. Different models and knowledge sources were established in order to solve different types of inventive problems, such as the forty inventive principles for eliminating the technical contradictions. These knowledge sources with different levels of abstraction are all built independent of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the inventive problem solving process, the development of an "intelligent knowledge manager" is explored in this thesis. On the one hand, according to the TRIZ knowledge sources ontologies, the manager offers to the users the relevant knowledge sources associated to the model they are building. On the other hand, the manager has the ability to fill "automatically" the models of the other knowledge sources. These research works aim at facilitating and automating the process of solving inventive problems based on semantic similarity and ontology techniques. At first, the TRIZ knowledge sources are formalized based on ontologies, such that heuristic inference can be executed to search for specific solutions. Then, methods for calculating semantic similarity are explored to search and define the missing links among the TRIZ knowledge sources. In order to solve inventive problems, the TRIZ user firstly chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other specific solutions are returned through ontology inference. Finally, a software prototype is developed based on semantic similarity and ontology inference to support this automatic process of solving inventive problems.

Project-based learning (PBL) is a teaching approach where students engage in the investigation of real-world problems through their inquiries. Studies found considerable support for PBL on student performance and improvement in grades K-12 and at the collegiate level. However, fewer studies have examined the effects of PBL at the collegiate level in comparison to K-12 education. No studies have examined the effects of PBL with preservice teachers taking educational psychology courses. The purpose of this study was to provide an analysis of PBL with preservice teachers taking educational psychology courses. An experiment was conducted throughout two semesters to evaluate student achievement and satisfaction in an undergraduate educational psychology child development course and in an undergraduate educational psychology assessments course, which included the same students from the first semester. Student achievement was determined using quantitative and qualitative analyses in each semester and longitudinally. Results in semester one indicated that the comparison group outperformed the PBL group. Results in semester two suggested there were no differences in instructional styles between groups. Longitudinal analyses showed that the comparison group declined in performance over time, whereas the PBL group improved over time; although, the comparison group still outperformed the PBL group. Results of this study indicate that PBL was not an influential teaching method for preservice teachers taking educational psychology courses.

Formulation of polyurethanes is a complex problem poorly understood as it has developed more as an art rather than a science. Only a few experts have mastered polyurethane (PU) formulation after years of experience and the major raw material manufacturers largely hold such expertise. Understanding of PU formulation is at present insufficient to be developed from first principles. The first principle approach requires time and a detailed understanding of the underlying principles that govern the formulation process (e.g. PU chemistry, kinetics) and a number of measurements of process conditions. Even in the simplest formulations, there are more that 20 variables often interacting with each other in very intricate ways. In this doctoral thesis the use of the Case-Based Reasoning and Artificial Neural Network paradigm is proposed to enable support for PUs formulation tasks by providing a framework for the collection, structure, and representation of real formulating knowledge. The framework is also aimed at facilitating the sharing and deployment of solutions in a consistent and referable way, when appropriate, for future problem solving. Two basic problems in the development of a Case-Based Reasoning tool that uses past flexible PU foam formulation recipes or cases to solve new problems were studied. A PU case was divided into a problem description (i. e. PU measured mechanical properties) and a solution description (i. e. the ingredients and their quantities to produce a PU). The problems investigated are related to the retrieval of former PU cases that are similar to a new problem description, and the adaptation of the retrieved case to meet the problem constraints. For retrieval, an alternative similarity measure based on the moment's description of a case when it is represented as a two dimensional image was studied. The retrieval using geometric, central and Legendre moments was also studied and compared with a standard nearest neighbour algorithm using nine different distance functions (e.g. Euclidean, Canberra, City Block, among others). It was concluded that when cases were represented as 2D images and matching is performed by using moment functions in a similar fashion to the approaches studied in image analysis in pattern recognition, low order geometric and Legendre moments and central moments of any order retrieve the same case as the Euclidean distance does when used in a nearest neighbour algorithm. This means that the Euclidean distance acts a low moment function that represents gross level case features. Higher order (moment's order>3) geometric and Legendre moments while enabling finer details about an image to be represented had no standard distance function counterpart. For the adaptation of retrieved cases, a feed-forward back-propagation artificial neural network was proposed to reduce the adaptation knowledge acquisition effort that has prevented building complete CBR systems and to generate a mapping between change in mechanical properties and formulation ingredients. The proposed network was trained with the differences between problem descriptions (i.e. mechanical properties of a pair of foams) as input patterns and the differences between solution descriptions (i.e. formulation ingredients) as the output patterns. A complete data set was used based on 34 initial formulations and a 16950 epochs trained network with 1102 training exemplars, produced from the case differences, gave only 4% error. However, further work with a data set consisting of a training set and a small validation set failed to generalise returning a high percentage of errors. Further tests on different training/test splits of the data also failed to generalise. The conclusion reached is that the data as such has insufficient common structure to form any general conclusions. Other evidence to suggest that the data does not contain generalisable structure includes the large number of hidden nodes necessary to achieve convergence on the complete data set.

As shown by the Macondo blowout, a deepwater well control event can result in loss of life, harm to the environment, and significant damage to company and industry reputation. Consistent adherence to safety regulations is a recurring issue in deepwater well construction. The two federal entities responsible for offshore U.S. safety regulation are the Department of the Interior’s Bureau of Safety and Environmental Enforcement (BSEE) and the U.S. Coast Guard (USCG), with regulatory authorities that span well planning, drilling, completions, emergency evacuation, environmental response, etc. With such a wide range of rules these agencies are responsible for, safety compliance cannot be comprehensively verified with the current infrequency of on-site inspections. Offshore regulation and operational safety could be greatly improved through continuous remote real-time data monitoring. Many government agencies have adopted monitoring regimes dependent on real-time data for improved oversight (e.g. NASA Mission Control, USGS Earthquake Early Warning System, USCG Vessel Traffic Services, etc.). Appropriately, real-time data monitoring was either re-developed or introduced in the wake of catastrophic events within those sectors (e.g. Challenger, tsunamis, Exxon Valdez, etc.). Over recent decades, oil and gas operators have developed Real-Time Operations Centers (RTOCs) for continuous, pro-active operations oversight and remote interaction with on-site personnel. Commonly seen as collaborative hubs, RTOCs provide a central conduit for shared knowledge, experience, and improved decision-making, thus optimizing performance, reducing operational risk, and improving safety. In particular, RTOCs have been useful in identifying and mitigating potential well construction incidents that could have resulted in significant non-productive time and trouble cost. In this thesis, a comprehensive set of recommendations is made to BSEE and USCG to expand and improve their regulatory oversight activities through remote real-time data monitoring and application of emerging real-time technologies that aid in data acquisition and performance optimization for improved safety. Data sets and tools necessary for regulators to effectively monitor and regulate deepwater operations (Gulf of Mexico, Arctic, etc.) on a continuous basis are identified. Data from actual GOM field cases are used to support the recommendations. In addition, the case is made for the regulator to build a collaborative foundation with deepwater operators, academia and other stakeholders, through the employment of state-of-the-art knowledge management tools and techniques. This will allow the regulator to do “more with less”, in order to address the fast pace of activity expansion and technology adoption in deepwater well construction, while maximizing corporate knowledge and retention. Knowledge management provides a connection that can foster a truly collaborative relationship between regulators, industry, and non-governmental organizations with a common goal of safety assurance and without confusing lines of authority or responsibility. This solves several key issues for regulators with respect to having access to experience and technical know-how, by leveraging industry experts who would not normally have been inaccessible. On implementation of the proposed real-time and knowledge management technologies and workflows, a phased approach is advocated to be carried out under the auspices of the Center for Offshore Safety (COS) and/or the Offshore Energy Safety Institute (OESI). Academia can play an important role, particularly in early phases of the program, as a neutral playing ground where tools, techniques and workflows can be tried and tested before wider adoption takes place. / text

Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the “with particle” (WP) group which showed contaminant particles and the “without particle” (WO) group which did not. Previous work effected the classification using only an adaptive Bayesian model. This work combines adaptive image quality modification with the adaptive Bayesian model. The first objective was to develop an off-line automated method for determining how to modify each individual raw image to obtain the quality required for improved classification results. This was done in a very novel way by defining image quality in terms of probability using a Bayesian classification model. The Nelder Mead Simplex method was then used to optimize the quality. The result was a “Reference Image Database” which was used as a basis for accomplishing the second objective. The second objective was to develop an in-line method for modifying the quality of new images to improve classification over that which could be obtained previously. Case Based Reasoning used the Reference Image Database to locate reference images similar to each new image. The database supplied instructions on how to modify the new image to obtain a better quality image. Experimental verification of the method used a variety of images from the extruder monitor including images purposefully produced to be of wide diversity. Image quality modification was made adaptive by adding new images to the Reference Image Database. When combined with adaptive classification previously employed, error rates decreased from about 10% to less than 1% for most images. For one unusually difficult set of images that exhibited very low local contrast of particles in the image against their background it was necessary to split the Reference Image Database into two parts on the basis of a critical value for local contrast. The end result of this work is a very powerful, flexible and general method for improving classification of digital images that utilizes both image quality modification and classification modeling.

Este trabalho apresenta o sistema de planejamento baseado em casos chamado FAR-OFF (Fast and Accurate Retrieval on Fast Forward). Este sistema usa o planejador FF (Fast-Forward) como um sistema generativo para adaptar os casos resgatados, bem como uma nova regra de similaridade chamada ADG, uma nova política de remoção de casos chamada Minimo-Prejuízo e um método de melhora da qualidade de um plano chamado SQUIRE. Todos esses novos métodos permitem um sistema de planejamento baseado em casos tão eficiente quanto os sistemas de planejamento baseados em busca heurística. / This work presents the FAR-OFF (Fast and Accurate Retrieval on Fast Forward) case-based planning system. This system uses the FF planner (Fast-Forward) as an effective generative system to adapt retrieved cases. It also uses a new similarity rule, called ADG, a new case-deletion policy named Minimal-Injury and a new method to improve the solution quality called SQUIRE. All these features are responsible for the results of the FAR-OFF system that are so efficient as the results of the heuristic search based planning systems.