INRECA offers tools and methods for developing, validating, and maintaining classification, diagnosis and decision support systems. INRECA's basic technologies are inductive and case-based reasoning . INRECA fully integrates  both techniques within one environment and uses the respective advantages of both technologies. Its object-oriented representation language CASUEL [10, 3] allows the definition of complex case structures, relations, similarity measures, as well as background knowledge to be used for adaptation. The objectoriented representation language makes INRECA a domain independent tool for its destined kind of tasks. When problems are solved via case-based reasoning, the primary kind of knowledge that is used during problem solving is the very specific knowledge contained in the cases. However, in many situations this specific knowledge by itself is not sufficient or appropriate to cope with all requirements of an application. Very often, background knowledge is available and/or necessary to better explore and interpret the available cases . Such general knowledge may state dependencies between certain case features and can be used to infer additional, previously unknown features from the known ones.
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.^
Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.
This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We show that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation. Finally, we show how other approaches from the literature can be classified according to the developed framework.
This paper is to present a new algorithm, called KNNcost, for learning feature weights for CBR systems used for classification. Unlike algorithms known so far, KNNcost considers the profits of a correct and the cost of a wrong decision. The need for this algorithm is motivated from two real-world applications, where cost and profits of decisions play a major role. We introduce a representation of accuracy, cost and profits of decisions and define the decision cost of a classification system. To compare accuracy optimization with cost optimization, we tested KNNacc against KNNcost. The first one optimizes classification accuracy with a conjugate gradient algorithm. The second one optimizes the decision cost of the CBR system, respecting cost and profits of the classifications. We present experiments with these two algorithms in a real application to demonstrate the usefulness of our approach.
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.
Although several systematic analyses of existing approaches to adaptation have been published recently, a general formal adaptation framework is still missing. This paper presents a step into the direction of developing such a formal model of transformational adaptation. The model is based on the notion of the quality of a solution to a problem, while quality is meant in a more general sense and can also denote some kind of appropriateness, utility, or degree of correctness. Adaptation knowledge is then defined in terms of functions transforming one case into a successor case. The notion of quality provides us with a semantics for adaptation knowledge and allows us to define terms like soundness, correctness and completeness. In this view, adaptation (and even the whole CBR process) appears to be a special instance of an optimization problem.
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.
Paris (Plan Abstraction and Refinement in an Integrated System) [4, 2] is a domain independent case-based planning system which allows the flexible reuse of planning cases by abstraction and refinement. This approach is mainly inspired by the observation that reuse of plans must not be restricted to a single description level. In domains with a high variation in the problems, the reuse of past solutions must be achieved at various levels of abstraction.
In this paper, we propose the PARIS approach for improving complex problem solving by learning from previous cases. In this approach, abstract planning cases are learned from given concrete cases. For this purpose, we have developed a new abstraction methodology that allows to completely change the representation language of a planning case, when the concrete and abstract languages are given by the user. Furthermore, we present a learning algorithm which is correct and complete with respect to the introduced model. An empirical study in the domain of process planning in mechanical engineering shows significant improvements in planning efficiency through learning abstract cases while an explanation-based learning method only causes a very slight improvement.