553 research outputs found

    Case Retrieval Nets as a Model for Building Flexible Information Systems

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    Im Rahmen dieser Arbeit wird das Modell der Case Retrieval Netze vorgestellt, das ein Speichermodell für die Phase des Retrievals beim fallbasierten Schliessen darstellt. Dieses Modell lehnt sich an Assoziativspeicher an, insbesondere wird das Retrieval als Rekonstruktion des Falles betrachtet anstatt als eine Suche im traditionellen Sinne. Zwei der wesentlichen Vorteile des Modells sind Effizienz und Flexibilität: Effizienz beschreibt dabei die Fähigkeit, mit grossen Fallbasen umzugehen und dennoch schnell ein Resultat des Retrievals liefern zu können. Im Rahmen dieser Arbeit wird dieser Aspekt formal untersucht, das Hauptaugenmerk ist aber eher pragmatisch motiviert insofern als der Retrieval-Prozess so schnell sein sollte, dass der Benutzer möglichst keine Wartezeiten in Kauf nehmen muss. Flexibilität betrifft andererseits die allgemeine Anwendbarkeit des Modells in Bezug auf veränderte Aufgabenstellungen, auf alternative Formen der Fallrepräsentation usw. Hierfür wird das Konzept der Informationsvervollständigung diskutiert, welches insbesondere für die Beschreibung von interaktiven Entscheidungsunterstützungssystemen geeignet ist. Traditionelle Problemlöseverfahren, wie etwa Klassifikation oder Diagnose, können als Spezialfälle von Informationsvervollständigung aufgefasst werden. Das formale Modell der Case Retrieval Netze wird im Detail erläutert und dessen Eigenschaften untersucht. Anschliessend werden einige möglich Erweiterungen beschrieben. Neben diesen theoretischen Aspekten bilden Anwendungen, die mit Hilfe des Case Retrieval Netz Modells erstellt wurden, einen weiteren Schwerpunkt. Diese lassen sich in zwei grosse Richtungen einordnen: intelligente Verkaufsunterstützung für Zwecke des E-Commerce sowie Wissensmanagement auf Basis textueller Dokumente, wobei für letzteres der Aspekt der Wiederbenutzung von Problemlösewissen essentiell ist. Für jedes dieser Gebiete wird eine Anwendung im Detail beschrieben, weitere dienen der Illustration und werden nur kurz erläutert. Zuvor wird allgemein beschrieben, welche Aspekte bei Entwurf und Implementierung eines Informationssystems zu beachten sind, welches das Modell der Case Retrieval Netze nutzt.In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable. Flexibility, on the other hand, is related to the general applicability of a case memory depending on the type of task to perform, the representation of cases etc. For this, the concept of information completion is discussed which allows to capture the interactive nature of problem solving methods in particular when they are applied within a decision support system environment. As discussed, information completion, thus, covers more specific problem solving types, such as classification and diagnosis. The formal model of CRNs is presented in detail and its properties are investigated. After that, some possible extensions are described. Besides these more theoretical aspects, a further focus is set on applications that have been developed on the basis of the CRN model. Roughly speaking, two areas of applications can be recognized: electronic commerce applications for which Case-Based Reasoning may provide intelligent sales support, and knowledge management based on textual documents where the reuse of problem solving knowledge plays a crucial role. For each of these areas, a single application is described in full detail and further case studies are listed for illustration purposes. Prior to the details of the applications, a more general framework is presented describing the general design and implementation of an information system that makes uses of the model of CRNs

    Building case-based reasoning applications with myCBR and COLIBRI Studio

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    myCBR and COLIBRI Studio are two well-established opensource frameworks for building case-based reasoning (CBR) applications, though they follow different approaches and support different phases of the CBR application development. In a nutshell: Where myCBR supports its users in developing a knowledge model for representing cases, it more or less leaves the software developers alone when they try to develop an application that uses the generated knowledge model. COLIBRI Studio, on the other hand, is focused in the development of applications that use that knowledge model. As soon as you have a knowledge model COLIBRI Studio offers templates for a variety of application types and supports in generating its source code. This paper explains the strengths and weaknesses of both frameworks regarding the rapid development of CBR applications. It also shows how to use both of them in conjunction

    Extending Knowledge Management to Mobile Workplaces

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    Knowledge and Knowledge Management (KM) are evolving into an increasingly eminent source of competitive advantage. However,for the time being, the potential of KM is usually limited to stationary workplaces. This excludes a multiplicity of mobile workers, many of them in charge of knowledge-intensive activities.This paper examines the capabilities and limitations of mobile technology usage in order to support KM. After a general overview of KM, the relevant mobile technology is introduced.Subsequently, the theory of mobile added values is employed to analyze the contributions of mobile technology for supporting KM in the different phases of the KM process. Especially the process of knowledge distribution is qualified to be supported through mobile technology.Knowledge Management; Mobile Commerce; Mobile Knowledge; Management; Mobile Business Processes; Mobile Added Values

    When to Explain? Model Agnostic Explanation Using a Case-based Approach and Counterfactuals

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    ExplainableArtificialIntelligence(XAI)systemshavegained importance with the increasing demand for understanding why and how an artificial intelligence system makes decisions. Counterfactual expla- nations, one of the rising trends of XAI, benefit from human counter- factual thinking mechanisms and aim to follow a similar way of rea- soning. In this paper, we create an eXplainable Case-Based Reasoning system using counterfactual samples with a model-agnostic approach. While CBR methodology allows us to use past experiences to create new explanations, using counterfactuals helps to increase understandability. The main idea of this paper is to generate an explanation when necessary. The proposed method is sample-centric. Thus, an adaptive explanation area is calculated for each data point in the dataset. We detect if there is any existing counterfactual of the samples to increase the coverage of the system, and we create explanation cases from detected sample- counterfactual pairs. If a query case is in the explanation area, at least one explanation case will be triggered, and a two-phase explanation will be created using a text template and a bi-directional bar graph. In this work, we will show (1) how explanation cases are created, (2) how the nature of a dataset influences the explanation area, (3) how understand- able explanations are created, and (4) how the proposed method works on open datasets

    Managing search engine optimisation experience using the INRECA methodology

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    This paper describes the reuse of Search Engine Optimisation (SEO) experience. The SEO domain is characterised by more than 200 factors leading to an obscurity of important factors. Such complex domains require experience-knowledge to enable the novice users adopt the domain. The Case Based Reasoning (CBR) approach is well suited to train new users in using this relatively new SEO technique to improve the visibility of their websites. Based on the principle of similarity, CBR enables the solution of similar recurring SEO problems for optimising websites for search engines. New users can effectively rely on SEO experience knowledge to solve new problems. Moreover, SEO techniques follow a similar procedure of implementation. Such procedural knowledge can be generalised and stored for future reference. For this purpose an experience base has been created to store SEO experience knowledge based on the principle of INRECA methodology. The experience is described using software process models. Until now the INRECA experience base has stored CBR system building experience. This research has extended the INRECA methodology for storing and retrieving SEO experience, taking into account the dynamic nature of the domain of SEO. An experiment illustrates the approach

    Using Design Patterns, Analysis Pattern, and Case-Based Reasoning to Improve Information Modeling and Method Engineering in Systems Development

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    Information modeling (IM) is the process of identifying information needs and models based onuser requirements and systems analysts’ perceptions during systems analysis and design. WhenIM is done correctly, it facilitates communication between the analysts and end-users about thefinal software product. In addition, successful IM provides a formal basis for both the analystsand the end-users about the tools and techniques that will be used in software development(SD), which, in turn, reduces costly overruns in time and money during systemsimplementation. Method engineering (ME) is the process of designing, constructing, andadapting information modeling methods for information systems development. As Siau (2003)and Kavakli (2005) point out that, while there has been a steady increase in IM and ME research(e.g. Kawalek & Wastell 2003, Kavakli 2005, Matulevicius 2005), most of the models reportedin recent literature are still primarily based on common sense approach, and, as a result, lack aslid theoretical foundation.This paper discusses the feasibility of combining design patterns (DPs), analysis patterns (APs) andcase-based reasoning (CBR) to improve information modeling and method engineering. Recentresearch in DP, AP, and CBR has proven that all those methods are effective in softwaredevelopment. In this paper, we propose a model that combines DP, AP and CBR as a tool toimprove IM and ME. We believe that the use of DP and AP, along with CBR will facilitate easiercommunication among systems analysts, end-users and software engineers thus improve on heefficiency in software development. In the paper, we also provide illustrative examples fromaccounting systems design to show the effectiveness of our proposed model. Finally, we provideevidence in this paper that the practical application of DPs, APs and CBR to systems developmentmakes it possible to identify and resolve critical issues and risks at earlier stages in IM and ME, andeventually lead to high quality end product

    CBR and MBR techniques: review for an application in the emergencies domain

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    The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

    Case-Based Decision Support for Disaster Management

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    Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

    A case-based reasoning approach to improve risk identification in construction projects

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    Risk management is an important process to enhance the understanding of the project so as to support decision making. Despite well established existing methods, the application of risk management in practice is frequently poor. The reasons for this are investigated as accuracy, complexity, time and cost involved and lack of knowledge sharing. Appropriate risk identification is fundamental for successful risk management. Well known risk identification methods require expert knowledge, hence risk identification depends on the involvement and the sophistication of experts. Subjective judgment and intuition usually from par1t of experts’ decision, and sharing and transferring this knowledge is restricted by the availability of experts. Further, psychological research has showed that people have limitations in coping with complex reasoning. In order to reduce subjectivity and enhance knowledge sharing, artificial intelligence techniques can be utilised. An intelligent system accumulates retrievable knowledge and reasoning in an impartial way so that a commonly acceptable solution can be achieved. Case-based reasoning enables learning from experience, which matches the manner that human experts catch and process information and knowledge in relation to project risks. A case-based risk identification model is developed to facilitate human experts making final decisions. This approach exploits the advantage of knowledge sharing, increasing confidence and efficiency in investment decisions, and enhancing communication among the project participants

    A hybrid approach for item collection recommendations : an application to automatic playlist continuation

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    Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version
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