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.

. Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method - captured in what is called the ACTIVATE-EXPLAIN-FOCUS cycle - is able to utilise a rich knowledge model in producing contextdependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples. 1 Introduction A growing part of the AI community is concerned with approaches that integrate several types of knowledge and reasoning methods (see for example [David et. al., 1993]). Although case-based reasoning is a rather new addition to the curre...

. 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 experimental 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. 1 Introduction Retrieval of sufficiently similar cases is one of the main points ...

The background of this paper is the area of case-based reasoning. This is a reasoning technique where one tries to use the solution of some problem which has been solved earlier in order to obtain a solution of a given problem. As example of types of problems where this kind of reasoning occurs very often is the diagnosis of diseases or faults in technical systems. In abstract terms this reduces to a classification task. A difficulty arises when one has not just one solved problem but when there are very many. These are called "cases" and they are stored in the case-base. Then one has to select an appropriate case which means to find one which is "similar" to the actual problem. The notion of similarity has raised much interest in this context. We will first introduce a mathematical framework and define some basic concepts. Then we will study some abstract phenomena in this area and finally present some methods developed and realized in a system at the University of Kaiserslautern.

We present the INRECA european project (ESPRIT 6322) on integration of induction and casebased reasoning (CBR) technologies for solving diagnostic tasks. A key distinction between casebased reasoning and induction is given in [1]: "In case-based methods, a new problem is solved by recognising its similarities to a specific known problem then transferring the solution of the known

Abstract. We propose in this paper a general framework for integrating inductive and case-based reasoning (CBR) techniques for diagnosis tasks. We present a set of practical integrated approaches realised between the KATE-Induction decision tree builder and the PATDEX case-based reasoning system. The integration is based on the deep understanding about the weak and strong points of each technology. This theoretical knowledge permits to specify the structural possibilities of a sound integration between the relevant components of each approach. We define different levels of integration called "cooperative", "workbench " and "seamless". They realise respectively a tight, medium and strong link between both techniques. Experimental results show the appropriateness of these integrated approaches for the treatment of noisy or unknown data. 1

As knowledge-based systems are addressing increasingly complex domains, their roles are shifting from classical expert systems to interactive assistants. To develop and maintain such systems, an integration of thorough knowledge acquisition procedures and sustained learning from experience is called for. A knowledge level modeling perspective has shown to be useful for analyzing the various types of knowledge related to a particular domain and set of tasks, and for constructing the models of knowledge contents needed in an intelligent system. To be able to meet the requirements of future systems with respect to robust competence and adaptive learning behavior, particularly in open and weak theory domains, a stronger emphasis should be put on the combined utilization of casespecific and general domain knowledge. In this chapter we present a framework for integrating KA and ML methods within a total knowledge modeling cycle, favoring an iterative rather than a top down approac...

We describe an approach for developing knowledge-based medical decision support systems based on the rather new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca+ project which particularly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis, as it was available among the partners, and ‘scale-up ’ to more general non-technical decision support tasks as typically given in medical domains. Inreca technology is used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases that are caused by psychotropes.

. A method for integrated case-based and generalization-based reasoning and learning is described. The primary role of general domain knowledge is to provide explanatory support for the case-based processes. A general explanation engine - the ACTIVATE-EXPLAIN-FOCUS cycle - utilizes a presumably rich, multirelational knowledge model in producing context-dependent explanations. 1. Introduction Case-based reasoning covers a wide variety of methods. While some methods emphasize problem solving and learning by use of specific cases instead of general domain knowledge, others use general knowledge 1 combined with cases. Among the latter, various approaches to what types of general knowledge to incorporate, as well as to how general knowledge is used, are taken. General knowledge may be used for an additional problem solving method, e.g. a method that is applied if the case-based method fails, and/or it may be used within the case-based method itself. The general knowledge may be of a s...

This paper presents Cabata, a hybrid case-based reasoning system that has been developed at the Department of Computer Science, Humboldt-University, Berlin. The most characteristic feature of the system is the combination of model-based and case-based reasoning within a hybrid architecture. 1 Introduction Within the framework of CBR research at the Department of Computer Science at Humboldt-University, Berlin, the Cabata-system has been --- and is still being --- developed. The system was designed to pay particular attention to the combination of domain-specific knowledge and classical CBR methods within a hybrid architecture. The cooperation of both, the rule-based and the case-based reasoning strategy, is expected to show significant improvements concerning all phases of CBR: ffl efficient case retrieval (using indexing), ffl the matching of cases, ffl storage of cases and meory organization, ffl learning beyond the scope of CBR. The key features of the Cabata-system are ffl in...