This technical report is a compilation of several papers on the task of solving diagnostic problems with the help of topology preserving maps. It first reviews the application of Kohonen's Self- Organizing Feature Map (SOFM) for a technical diagnosis task, namely the fault detection in CNC-Machines with the KoDiag system [RW93], [RW94]. For emergent problems with coding attribute values, we then introduce fuzzy coding, similarity assignment and weight updating schemes for three crucial data types (continuous values, ordered and unordered symbols). These techniques result in a SOFM type network based on user defined local similarities, thus being able to incorporate a priori knowledge about the domain [Rah95].
We are going to present two methods that allow to exploit previous expe-rience in the area of automated deduction. The first method adapts (learns)the parameters of a heuristic employed for controlling the application of infer-ence rules in order to find a known proof with as little redundant search effortas possible. Adaptation is accomplished by a genetic algorithm. A heuristiclearned that way can then be profitably used to solve similar problems. Thesecond method attempts to re-enact a known proof in a flexible manner in orderto solve an unknown problem whose proof is believed to lie in (close) vicinity.The experimental results obtained with an equational theorem prover show thatthese methods not only allow for impressive speed-ups, but also make it possibleto handle problems that were out of reach before.
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.^
Im Rahmen des Sonderforschungsbereichs SFB314, Projekt X9 "Lernen und Analogie in technischen Expertensystemen", wurde die Verwendbarkeit von Techniken des fallbasierten Schliessens in wissens- basierten Systemen untersucht. Als prototypische Anwendungsdomäne wurde die Arbeitsplanerstellung rotationssymmetrischer Werkstücke gewählt. Im vorliegenden Beitrag wird ein Modell der Arbeits- planerstellung unter Berücksichtigung der verschiedenen, bisher als unabhängig behandelten Planungsmethoden beschrieben. Auf der Basis einer modelbasierte Wissensakquistion aus in Unternehmen verfügbaren Arbeitsplänen wird ein Ausschnitt der Arbeitsplanerstellung, die Aufspannplanung, detailliert. Die Anwendbarkeit wurde durch eine prototypische Realisierung nachgewiesen.