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- case-based problem solving (4)
- Knowledge Acquisition (3)
- explanation-based learning (3)
- Case-Based Reasoning (2)
- learning system (2)
- Abstraction (1)
- Adaption (1)
- COMOKIT (1)
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Fallbasiertes Schliessen zur Kreditwürdigkeitsprüfung
(1995)
- In diesem Artikel diskutieren wir Anforderungen aus der Kreditwürdigkeitsprüfung und ihre Erfüllung mit Hilfe der Technik des fallbasierten Schliessens. Innerhalb eines allgemeinen Ansatzes zur fallbasierten Systementwicklung wird ein Lernverfahren zur Optimierung von Entscheidungskosten ausführlich beschrieben. Dieses Verfahren wird, auf der Basis realer Kundendaten, mit dem fallbasierten Entwicklungswerkzeug INRECA empirisch bewertet. Die Voraussetzungen für den Einsatz fallbasierter Systeme zur Kreditwürdigkeitsprüfung werden abschliessend dargestellt und ihre Nüt zlichkeit diskutiert.
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Fallbasiertes Schliessen zur Unterstützung der Wiederverwendung objektorientierter Software: Eine Fallstudie
(1995)
- Bei der Erstellung komplexer Software spielt die Wiederverwendung vorhandener Programmbestandteile eine besonders grosse Rolle, da hierdurch sowohl die Software-Qualität gesteigert, als auch der gesamte Erstellungsund Wartungsaufwand erheblich reduziert werden kann. In jüngster Zeit gewinnen objektorientierte Programmiersprachen zunehmend an Bedeutung, da die Wiederverwendung hierbei bereits durch Sprachkonzepte wie z.B. Vererbung und Polymorphie unterstützt wird. Weiterhin besteht jedoch das Problem, zur Wiederverwendung geeignete Programmbestandteile aufzufinden. Ziel dieser Arbeit ist es herauszufinden, inwieweit fallbasiertes Schliessen nach dem aktuellen Stand der Kunst die Wiederverwendung objektorientierter Software unt erstützen kann. Hierzu wurde eine entsprechende Anwendung prototypisch auf der Basis des INRECA-Systems entwickelt. Durch ausgewählte Testsituationen wurden Erfahrungen mit diesem Prototyp gesammelt und systematisch ausgewertet.
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Building and Refining Abstract Planning Cases by Change of Representation Language
(1999)
- Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description - as used in most hierarchical planners - has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of PARIS (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.^
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Methodology for Building CBR Applications
(1998)
- As the previous chapters of this book have shown, case-based reasoning is a technology that has been successfully applied to a large range of different tasks. Through all the different CBR projects, both basic research projects as well as industrial development projects, lots of knowledge and experience about how to build a CBR application has been collected. Today, there is already an increasing number of successful companies developing industrial CBR applications. In former days, these companies could develop their early pioneering CBR applications in an ad-hoc manner. The highly-skilled CBR expert of the company was able to manage these projects and to provide the developers with the required expertise.
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Explanation-based Similarity: A Unifying Approach for Integrating Domain Knowledge into Case-based Reasoning for Diagnosis and Planning Tasks
(1993)
- Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.
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On the Role of Abstraction in Case-Based Reasoning
(1996)
- This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We show that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation. Finally, we show how other approaches from the literature can be classified according to the developed framework.
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Considering Decision Cost During Learning of Feature Weights
(1996)
- This paper is to present a new algorithm, called KNNcost, for learning feature weights for CBR systems used for classification. Unlike algorithms known so far, KNNcost considers the profits of a correct and the cost of a wrong decision. The need for this algorithm is motivated from two real-world applications, where cost and profits of decisions play a major role. We introduce a representation of accuracy, cost and profits of decisions and define the decision cost of a classification system. To compare accuracy optimization with cost optimization, we tested KNNacc against KNNcost. The first one optimizes classification accuracy with a conjugate gradient algorithm. The second one optimizes the decision cost of the CBR system, respecting cost and profits of the classifications. We present experiments with these two algorithms in a real application to demonstrate the usefulness of our approach.
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Collecting Experience on the Systematic Development of CBR Applications using the INRECA Methodology
(1999)
- This paper presents an overview of the INRECA methodology for building and maintaining CBR applications. This methodology supports the collection and reuse of experience on the systematic development of CBR applications. It is based on the experience factory and the software process modeling approach from software engineering. CBR development experience is documented using software process models and stored in different levels of generality in a three-layered experience base. Up to now, experience from 9 industrial projects enacted by all INRECA II partners has been collected.
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Similarity Measures for Object-Oriented Case Representations
(1998)
- Object-oriented case representations require approaches for similarity assessment that allow to compare two differently structured objects, in particular, objects belonging to different object classes. Currently, such similarity measures are developed more or less in an ad-hoc fashion. It is mostly unclear, how the structure of an object-oriented case model, e.g., the class hierarchy, influences similarity assessment. Intuitively, it is obvious that the class hierarchy contains knowledge about the similarity of the objects. However, how this knowledge relates to the knowledge that could be represented in similarity measures is not obvious at all. This paper analyzes several situations in which class hierarchies are used in different ways for case modeling and proposes a systematic way of specifying similarity measures for comparing arbitrary objects from the hierarchy. The proposed similarity measures have a clear semantics and are computationally inexpensive to compute at run-time.
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Integrating General Knowledge with Object-Oriented Case Representation and Reasoning
(1996)
- When problems are solved through reasoning from cases, the primary kind of knowledge is contained in the specific cases which are stored in the case base. However, in many situations additional background-knowledge is required to cope with the requirements of an application. We describe an approach to integrate such general knowledge into the reasoning process in a way that it complements the knowledge contained in the cases. This general knowledge itself is not sufficient to perform any kind of model-based problem solving, but it is required to interpret the available cases appropriately. Background knowledge is expressed by two different kinds of rules that both must be formalized by the knowledge engineer: Completion rules describe how to infer additional features out of known features of an old case or the current query case. Adaptation rules describe how an old case can be adapted to fit the current query. This paper shows how these kinds of rules can be integrated into an object-oriented case representation.
