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Case-based knowledge acquisition, learning and problem solving for diagnostic real world tasks
(1999)
Within this paper we focus on both the solution of real, complex problems using expert system technology and the acquisition of the necessary knowledge from a case-based reasoning point of view. The development of systems which can be applied to real world problems has to meet certain requirements. E.g., all available information sources have to be identified and utilized. Normally, this involves different types of knowledge for which several knowledge representation schemes are needed, because no scheme is equally natural for all sources. Facing empirical knowledge it is important to complement the use of manually compiled, statistic and otherwise induced knowledge by the exploitation of the intuitive understandability of case-based mechanisms. Thus, an integration of case-based and alternative knowledge acquisition and problem solving mechanisms is necessary. For this, the basis is to define the "role" which case-based inference can "play" within a knowledge acquisition workbench. We will discuss a concrete casebased architecture, which has been applied to technical diagnosis problems, and its integration into a knowledge acquisition workbench which includes compiled knowledge and explicit deep models, additionally.
Im Bereich der Expertensysteme ist das Problemlösen auf der Basis von Fallbeispielen ein derzeit sehr aktuelles Thema. Da sich sehr unterschiedliche Fachgebiete und Disziplinen hiermit auseinandersetzen, existiert allerdings eine entsprechende Vielfalt an Begriffen und Sichten auf fallbasiertes Problemlösen. In diesem Beitrag werden wir einige für das fallbasierte Problemlösen wichtige Begriffe präzisieren bzw. begriffliche Zusammenhänge aufdecken. Die dabei verfolgte Leitlinie ist weniger die, ein vollständiges Begriffsgebäude zu entwickeln, sondern einen ersten Schritt in Richtung eines einfachen Beschreibungsrahmens zu gehen, um damit den Vergleich verschiedener Ansätze und Systeme zu ermöglichen. Auf dieser Basis wird dann der derzeitige Stand der Forschung am Beispiel konkreter Systeme zur fallbasierten Diagnose dargelegt. Den Abschluss bildet eine Darstellung bislang offener Fragen und interessanter Forschungsziele.
Fallbasiertes Schliessen ist ein derzeit viel diskutierter Problemlösesansatz. Dieser Beitrag gibt einen Überblick über den aktuellen Stand der Forschung auf diesem Gebiet, insbesondere im Hinblick auf die Entwicklung von Expertensystemen (einen ersten Schritt in diese Richtung stellte bereits der Beitrag von Bartsch-Spörl, [BS87] dar). Dazu stellen wir die dem fallbasierten Schliessen zugrundeliegenden Mechanismen vor. Ergänzt wird dies durch den Vergleich mit alternativen Verfahren wie z.B. regelbasiertes, analoges und induktives Schliessen sowie eine ausführliche Literaturübersicht.
Retrieval of cases is one important step within the case-based reasoning paradigm. We propose an improvement of this stage in the process model for finding most similar cases with an average effort of O[log2n], n number of cases. The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k-d tree, for efficient case retrieval according to a given similarity measure sim. In addition to illustrating the basic idea, we present the expe- rimental results of a comparison of four different k-d tree generating strategies as well as introduce the notion of virtual bounds as a new one that significantly reduces the retrieval effort from a more pragmatic perspective. The presented approach is fully implemented within the (Patdex) system, a case-based reasoning system for diagnostic applications in engineering domains.
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.
This paper motivates the necessity for support for negotiation during Sales Support on the Internet within Case-Based Reasoning solutions. Different negotiation approaches are discussed and a general model of the sales process is presented. Further, the tradition al CBR-cycle is modified in such a way that iterative retrieval during a CBR consulting session is covered by the new model. Several gen eral characteristics of negotiation are described and a case study is shown where preliminary approaches are used to negotiate with a cu stomer about his demands and available products in a 'CBR-based' Electronic Commerce solution.
Contrary to symbolic learning approaches, that represent a learned concept explicitly, case-based approaches describe concepts implicitly by a pair (CB; sim), i.e. by a measure of similarity sim and a set CB of cases. This poses the question if there are any differences concerning the learning power of the two approaches. In this article we will study the relationship between the case base, the measure of similarity, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the hypothesis of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.
In den letzten Jahren wurden Methoden des fallbasierten Schliessens häufig in Bereichen verwendet, in denen traditionell symbolische Verfahren zum Einsatz kommen, beispielsweise in der Klassifikation. Damit stellt sich zwangsläufig die Frage nach den Unterschieden bzw. der Mächtigkeit dieser Lernverfahren. Jantke [Jantke, 1992] hat bereits Gemeinsamkeiten von Induktiver Inferenz und fallbasierter Klassifikation untersucht. In dieser Arbeit wollen wir einige Zusammenhänge zwischen der Fallbasis, dem Ähnlichkeitsmass und dem zu erlernenden Begriff verdeutlichen. Zu diesem Zweck wird ein einfacher symbolischer Lernalgorithmus (der Versionenraum nach [Mitchell, 1982]) in eine äquivalente, fallbasiert arbeitende Variante transformiert. Die vorgestellten Ergebnisse bestätigen die Äquivalenz von symbolischen und fallbasierten Ansätzen und zeigen die starke Abhängigkeit zwischen dem im System verwendeten Mass und dem zu lernenden Begriff.