Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case- based reasoning, describes some of the leading methodo- logical approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summa-rized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework 1 General Considerations Patdex 1 is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench 2 for technical diagnosis, which was developed at the university of Kaiserslautern over the past years (cf. e.g. [4, 5, 23]), Moltke contains other parts as well (cf. e.g. [16]), in particular a model-based approach (cf. [21, ...

. If knowledge-based systems are to become more competent and robust in solving real world problems, they need to be able to adapt to an evolving domain and a changing environment. This paper proposes a computational model - a framework -for knowledge-intensive problem solving and learning from experience. The model has been instantiated in an architecture for knowledge-intensive case-based reasoning and learning called CREEK (Case-based Reasoning through Extensive Expert Knowledge). The importance of a thorough, extensive knowledge model to support the reasoning and learning processes is emphasized. In case-based reasoning a problem is solved by retrieving a similar past problem case, and using this case in solving the new problem. Learning becomes a process of extracting relevant information from a problem just solved, and integrating the new case into the existing case-base. The computational model presented combines case-based learning and reasoning with model-based and rule-based ...

Recently, there has been renewed interest in the use of exemplar-based schemes for concept representation and learning. In this paper, we compare systems learning concepts represented in this form with those which learn concepts represented by decision rules, such as the ID3 and AQ11 rule induction systems. We aim to clarify the distinction between the two representational schemes, and compare how systems based on the different schemes address the problem of learning within finite resources. Our conclusions

Abstract — We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self-organizing maps, an unsupervised neural learning approach.

Abstract — We report on the results of a pilot study in which a datamining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chisquared test, and the use of selforganizing maps, an unsupervised neural learning approach.