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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.
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.
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.
Forschungsprojekte im Bereich des fallbasierten Schliessens in den USA, die Verfügbarkeit kommerzieller fallbasierter Shells, sowie erste Forschungsergebnisse initialer deutscher Projekte haben auch in Deutschland verstärkte Aktivitäten auf dem Gebiet des fallbasierten Schliessens ausgelöst. In diesem Artikel sollen daher Projekte, die sich als Schwerpunkt oder als Teilaspekt mit fallbasierten Aspekten beschäftigen, einer breiteren Öffentlichkeit kurz vorgestellt werden.
While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB; d), i.e. by a set CB of cases and a distance measure d. Given the same information, symbolic as well as the case-based approach compute a classification when a new case is presented. This poses the question if there are any differences concerning the learning power of the two approaches. In this work we will study the relationship between the case base, the measure of distance, 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 conjecture 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.
We describe a hybrid architecture supporting planning for machining workpieces. The architecture is built around CAPlan, a partial-order nonlinear planner that represents the plan already generated and allows external control decision made by special purpose programs or by the user. To make planning more efficient, the domain is hierarchically modelled. Based on this hierarchical representation, a case-based control component has been realized that allows incremental acquisition of control knowledge by storing solved problems and reusing them in similar situations.
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.
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.