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.

Fixtures accurately locate and secure a part during machining operations such that the part can be manufactured to design specifications. To reduce the design costs associated with fixturing, various computer-aided fixture design (CAFD) methods have been developed through the years to assist the fixture designer. Much research has been directed towards developing systems that determine an optimal fixture plan layout, but there is still a need to develop a CAFD method that can continue to assist designers at the unit level where the key task is identifying the appropriate structure that the individual units comprising a fixture should take. This research work details the development of a CAFD methodology (called CAFixD) that seeks to fill this hole in the CAFD field. The approach taken is to consider all operational requirements of a fixture problem, and use them to guide the design of a fixture at the unit level. Based upon a case-based reasoning (CBR) methodology where relevant design experience is retrieved and adapted to provide a new fixture design solution, the CAFixD methodology adopts a rigorous approach to indexing design cases in which axiomatic design functional requirement decomposition is adopted. Thus, the design requirement is decomposed in terms of functional requirements, physical solutions are retrieved and adapted for each individual requirement, and the design re-constituted to form a complete fixture design. Case adaptation knowledge is used to guide the retrieval process. Possible adaptation strategies for modifying candidate cases are identified and then evaluated. Case and adaptation strategy combinations that result in adapted designs that best satisfy the preferences of the designer are used as the final design solutions. Possible means of refining the effectiveness of the method include combining adaptation strategies and considering the order in which design decisions are taken.

Thesis (doctoral)--Universität Kaiserslautern, 2002. / Includes bibliographical references (p. 219-234) and index.

Professors need alternative programs to support their online teaching. This dissertation reports an initial study in a long-term research agenda for developing a faculty online teaching solution. The primary purpose of the study is to explore faculty perceptions of a case library to help decision makers and researchers determine whether they would pursue the use of such a tool to support faculty online teaching. The secondary purpose of the study is to generate design knowledge to inform future development of and research on this or similar case libraries. The methodology of this study includes three components: development research, rapid prototyping, and qualitative methods. Development research and rapid prototyping provided a three-stage framework for this study: conceptualization, development, and research. I synthesized the literature to create conceptual models of an Online Teaching Case Library (OTCL) at the conceptualization stage, built a prototype to implement the models at the development stage, and conducted research to evaluate the prototype at the research stage. Qualitative methods guided data gathering and analysis. I recruited seven faculty participants based on a purposeful sampling technique. To gather the data, I followed a three-step data collection process: initial interviews, contextual interviews, and final interviews. This process allowed me to observe and interview faculty participants while they were exploring the prototype. I analyzed the data by following an 11-step procedure synthesized from the works of Miles and Huberman (1994) as well as LeCompte and Schensul (1999a). This study found that on one hand, faculty members might use an OTCL, because they perceived that this tool could support their apprenticeship approach to learning to teach. On the other hand, however, their perceived decision to use an OTCL would also be influenced by the perceptions of the usefulness and usability of the tool. The study identified the initial evidence supporting an OTCL as an online teaching resource and the challenges involved in developing and implementing such a solution. It provides a base for decision makers to determine whether they would adopt this tool. It also offers some design guidance for those who do want to pursue this solution to faculty development.

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.

As long as there have been computers, one goal has been to be able to communicate with them using natural language. It has turned out to be very hard to implement a dialog system that performs as well as a human being in an unrestricted domain, hence most dialog systems today work in small, restricted domains where the permitted dialog is fully controlled by the system. In this thesis we present two dialog systems for communicating with an autonomous agent: The first system, the WITAS RDE, focuses on constructing a simple and failsafe dialog system including a graphical user interface with multimodality features, a dialog manager, a simulator, and development infrastructures that provides the services that are needed for the development, demonstration, and validation of the dialog system. The system has been tested during an actual flight connected to an unmanned aerial vehicle. The second system, CEDERIC, is a successor of the dialog manager in the WITAS RDE. It is equipped with a built-in machine learning algorithm to be able to learn new phrases and dialogs over time using past experiences, hence the dialog is not necessarily fully controlled by the system. It also includes a discourse model to be able to keep track of the dialog history and topics, to resolve references and maintain subdialogs. CEDERIC has been evaluated through simulation tests and user tests with good results. / <p>Report code: LiU{Tek{Lic{2006:29.</p>

<p>This thesis describes one promising approach where a problem of time<br />series analysis and prediction was solved by using Case Based Reasoning<br />(CBR) technology. Foundations and main concepts of this technology are<br />described in detail. Furthermore, a detailed study of different approaches in<br />time series analysis is given. System CuBaGe (Curve Base Generator) - A<br />robust and general architecture for curve representation and indexing time<br />series databases, based on Case based reasoning technology, was<br />developed. Also, a corresponding similarity measure was modelled for a<br />given kind of curve representation. The presented architecture may be<br />employed equally well not only in conventional time series (where all<br />values are known), but also in some non-standard time series (sparse,<br />vague, non-equidistant). Dealing with the non-standard time series is the<br />highest advantage of the presented architecture.</p> / <p>U ovoj doktorskoj disertaciji prikazan je interesantan i perspektivan pristup<br />re&scaron;avanja problema analize i predviđanja vremenskih serija kori&scaron;ćenjem<br />Case Based Reasoning (CBR) tehnologije. Detaljno su opisane osnove i<br />glavni koncepti ove tehnologije. Takođe, data je komparativna analiza<br />različitih pristupa u analizi vremenskih serija sa posebnim osvrtom na<br />predviđanje. Kao najveći doprinos ove disertacije, prikazan je sistem<br />CuBaGe (Curve Base Generator) u kome je realizovan originalni način<br />reprezentacije vremenskih serija zajedno sa, takođe originalnom,<br />odgovarajućom merom sličnosti. Robusnost i generalnost sistema<br />ilustrovana je realnom primenom u domenu finansijskog predviđanja, gde<br />je pokazano da sistem jednako dobro funkcioni&scaron;e sa standardnim, ali i sa<br />nekim nestandardnim vremenskim serijama (neodređenim, retkim i<br />neekvidistantnim).</p>

Knowledge and knowledge work are key factors of today’s successful companies. This study devises an approach for increasing the performance of knowledge work by shifting it towards a process orientation. Business process management and workflow management are methods for structured and predefined work but are not flexible enough to support knowledge work in a comprehensive way. Case-based reasoning (CBR) uses the knowledge of previously experienced cases in order to propose a solution to a problem. CBR can be used to retrieve, reuse, revise, retain and store functional and process knowledge. The aim of the research was to develop an approach that combines CBR and process execution to improve knowledge work. The research goals are: a casedescription for knowledge work that can be integrated into a process execution system and that contains both functional and process knowledge; a similarity algorithm for the retrieval of functional and procedural knowledge; and an adaptation mechanism that deals with the different granularities of solution parts. This thesis contains a profound literature framework and follows a design science research (DSR) strategy. During the awareness phase of the design science research process, an application scenario was acquired using the case study research method, which is the admission process for a study programme at a university. This application scenario is used to introduce and showcase the combined CBR and process execution approach called ICEBERG-PE, which consists of a case model and CBR services. The approach is implemented as a prototype and can be instantiated using the ICEBERG-PE procedure model, a specific procedure model for ontology-based, CBR projects. The ICEBERG-PE prototype has been evaluated using triangulated evaluation data and different evaluation settings to confirm that the approach is transferable to other contexts. Finally, this thesis concludes with potential recommendations for future research. / Computing / D. Phil. (Information Systems)

No description available.

The main purpose of this study was to design and develop a suitable and authentic online case-based learning environment for the classroom management course and to investigate the preservice teachers&rsquo / learning experience in this environment specifically in terms of problems solving skills, motivation, study habits and self-confidence. Having this purpose in mind, action research method in the form of qualitative research methodology was conducted. The main characteristic of the action research is its active and practical nature. Action research has a spiral structure and continues through action cycles. In present study, three action cycles were conducted in a spiral process. The participants of the study were 32 Elementary Science Education students (7 males and 25 females) who were third-year and enrolled classroom management course. Before the implementation, the researcher analyzed current case-based learning environments and methods in teacher education and developed the first version of the VOCABLE. During the implementation the researcher, as the assistant instructor of the course, led VOCABLE practices and made necessary revisions on VOCABLE. Through action cycles data were collected by group interviews, personal interviews, video records of the implementations, expectation and evaluation questionnaires, VOCABLE logs and, electronic posts. The data were analyzed by using descriptive and content v analysis techniques. Results showed that VOCABLE solved the preservice teachers practice problem in classroom management course. Specifically, VOCABLE contributed preservice teachers&rsquo / problem solving abilities, motivation and self-confidence. Also it helped them to be used to teaching profession. On the other hand VOCABLE did not affect their study habits.