This chapter provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research.

. In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study. 1 Introduction Case-based reasoning (CBR) systems solve new problems by retrieving prior solutions of similar previous problems and performi...

The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very di cult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring exible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the bene ts of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.

Case-based reasoning research on indexing and retrieval focuses primarily on developing specific retrieval criteria, rather than on developing mechanisms by which such criteria can be learned as needed. This paper presents a framework for learning to refine indexing criteria by introspective reasoning. In our approach, a self-model of desired system performance is used to determine when and how to refine retrieval criteria. We describe the advantages of this approach for focusing learning on useful information even in the absenceof explicit processing failures, and support its benefits with experimental results on how an implementation of the model affects performance of a case-based planning system. Introduction Case-based reasoning (CBR) has been widely investigated both for its practical applications and as a model of human reasoning and learning (see Kolodner (1993) for an overview). One relevant facet of human reasoning, however, has received little attention in case-based reason...

Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the...

This position paper sketches a framework for modeling introspective reasoning and discusses the relevance of that framework for modeling introspective reasoning about memory search. It argues that effective and flexible memory processing in rich memories should be built on five types of explicitly represented self-knowledge: knowledge about information needs, relationships between different types of information, expectations for the actual behavior of the information search process, desires for its ideal behavior, and representations of how those expectations and desires relate to its actual performance. This approach to modeling memory search is both an illustration of general principles for modeling introspective reasoning and a step towards addressing the problem of how a reasoner— human or machine—can acquire knowledge about the properties of its own knowledge base.

Abstract not found

Guiding the generation of abductive explanations is a difficult problem. Applying casebased reasoning to abductive explanation generation---generating new explanations by retrieving and adapting explanations for prior episodes---offers the benefit of re-using successful explanatory reasoning but raises new issues concerning how to perform similarity assessment to judge the relevance of prior explanations to new situations. Similarity assessment affects two points in the case-based explanation process: deciding which explanations to retrieve and evaluating the retrieved candidates. We address the problem of identifying similar explanations to retrieve by basing that similarity assessment on a categorization of anomaly types. We show that the problem of evaluating retrieved candidate explanations is often impeded by incomplete information about the situation to be explained, and address that problem with a novel similarity assessment method which we call constructive similarity assessme...

The case-based reasoning paradigm models how reuse of stored experiences contributes to expertise. In a case-based problem-solver, new problems are solved by retrieving stored information about previous problem-solving episodes and adapting it to suggest solutions to the new problems. The results are then themselves added to the reasoner's memory in new cases for future use. Despite this emphasis on learning from experience, however, experience generally plays a minimal role in models of how the case-based reasoning process is itself performed. Case-based reasoning systems generally do not refine the methods they use to retrieve or adapt prior cases, instead relying on static pre-defined procedures. The thesis of this article is that learning from experience can play a key role in building expertise by refining the case-based reasoning process itself. To support that view and to illustrate the practicality of learning to refine case-based reasoning, this article presents ongoing resear...

Case-based reasoning (CBR) is an artificial intelligence methodology that uses specific encapsulated prior experiences as a basis for reasoning about similar new situations. CBR systems rely on various "knowledge containers," such as the case-base of prior experiences and similarity criteria for comparing situations and retrieving the most relevant cases. Explicit or implicit changes in the reasoning environment, task focus, and user base may influence the fit of the current knowledge state to the task context, which can affect the quality and efficiency of reasoning results. Over time, the knowledge containers may need to be updated in order to maintain or improve performance in response to changes in task or environment. In particular, maintaining the case-base --- the traditional mainstay of knowledge underlying CBR systems -- is essential for preserving and expanding the capability of a CBR system throughout its life-cycle. This dissertation provides a first coherent picture of the overall case-base maintenance problem in CBR and develops new case-base maintenance techniques within that paradigm. The thesis presents a theoretical framework for describing case-base maintenance techniques according to the types of maintenance policies implemented by a given system. The framework serves to unify current maintenance practice, to point out areas for new fundamental research, and as a step toward recommending the best maintenance practices for varying system performance goals. In that context, the thesis goes on to make an examination and account of underlying regularity assumptions in the CBR process that directly affect maintenance activity. The theoretical picture of case-base maintenance is then complemented with a presentation of new methods and experiments in applied case-base...