Abstract. CBR (case-based reasoning) has considerable potential for developing intelligent assistants for the World Wide Web. Several case-based intelligent applications already exist on the web but these applications follow a thin client model with the intelligence located at the server side. In this paper we explore the advantages of making these applications more distributed. We illustrate the kinds of application where the dialogue with the case-base is long-lived and network latency or server load would suggest that some of the processing should be distributed to the client side. We present an architecture for such a distributed CBR system and describe how a case-representation language based on XML can facilitate this distribution. The advantages of adopting an XML case-representation language are interoperability and flexibility of reuse and these are discussed in the second half of the paper. 1.

. Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected bene#ts of this learning process depend on twotypes of regularity: #1# problem-solution regularity, the relationship between problem-to-problem and solution-to-solution similaritymeasures that assures that solutions to similar prior problems are a useful starting point for solving similar current problems, and #2# problemdistribution regularity, the relationship between old and new problems that assures that the case library will contain cases similar to the new problems it encounters. Unfortunately, these types of regularity are not assured. Even in contexts for which initial regularityissu#cient, problems may arise if a system's users, tasks, or external environmentchange over time. This paper de#nes criteria for assessing the twotypes of regularity, discusses how the de#nitions may be used to assess the need...

One problem with using CBR for diagnosis is that for many diagnosis tasks a full case description may not be available at the beginning of the diagnosis. The standard CBR methodology requires a detailed case description in order to perform case retrieval and this may not be practical in medical or technical diagnosis. In this paper we describe medical and electronic diagnosis tasks where many features may make up a case description but only a few of these features will be required in an individual diagnosis. We introduce an incremental CBR mechanism that can initiate case retrieval with a skeletal case description and will elicit extra discriminating information on the target case during the diagnostic process. This incremental CBR mechanism is evaluated in a system for troubleshooting a microprocessor board. Keywords: Case-based reasoning, case retrieval, fault diagnosis, medical diagnosis. 2 An Incremental Case Retrieval Mechanism for Diagnosis Abstract One problem with using CB...

The work reported in this thesis is motivated by the belief that knowledge-based systems (KBS) research needs to focus more on users ’ needs and cater for the various decision situations in which users will find themselves. To build individual systems that cater for all the activities that may be needed is not feasible or desirable. The problems associated with capturing knowledge are well known and the ability to capture knowledge once and access and manipulate the knowledge in multiple ways is highly desirable. It adds value to the original knowledge and offers all the benefits associated with the reuse of resources. Thus, the problem becomes one of knowledge reuse. The research question pursued in this thesis is “can knowledge captured for one purpose, such as consultation, be reused to support a wide range of alternative purposes, such as critquing or tutoring, allowing the user to answer different types of questions according to their current circumstances”? Further, this question was to be answered in a situated cognition, dynamic knowledge framework. The system developed in this thesis is based on the Multiple Classification Ripple Down Rule (MCRDR) knowledge acquisition and representation technique. MCRDR is a form of casedbased

CBR seems well suited to fault diagnosis because diagnostic episodes naturally form cases and much of expert competence seems to be based on reuse of old solutions.

One problem with using CBR for diagnosis is that a full case description may not be available at the beginning of the diagnosis. The standard CBR methodology requires a detailed case description in order to perform case retrieval and this is often not practical in diagnosis. We describe two fault diagnosis tasks where many features may make up a case description but only a few features are required in an individual diagnosis. We evaluate an incremental CBR mechanism that can initiate case retrieval with a skeletal case description and will elicit extra discriminating information during the diagnostic process. Keywords: Case-based reasoning, case retrieval, electronic fault diagnosis. 2 1 Introduction The fact that human problem solving competence is often based on reasoning from examples supports the use of case-based reasoning (CBR) for developing knowledge-based systems. In particular, good performance in both technical and medical diagnosis is often dependent on remembering simi...

Variant design refers to the technique of adapting existing design specifications to satisfy new design goals and constraints. Specific support of variant design techniques in current computer aided design systems would help to realize a rapid response manufacturing environment. A survey of approaches supporting variant design is presented. Capabilities used in current commercial computer aided design systems are discussed along with approaches used in recent research efforts. Information standards applicable to variant design are also identijed. Barriers to variant design in current systems are ident#ed and ideas are presented for augmentation of current systupzs to support uariant design.

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...

This tutorial article introduces the concepts and applications of Case-based Reasoning (CBR) systems. The first section briefly describes what is CBR, when to use CBR and why use CBR. The second section looks at the description and indexing of cases in CBR systems. The retrieval and adaptation processes for finding solutions are outlined in section three. Learning and maintenance of CBR, due to the changes in domain knowledge and task environments over time, are given in section four. The role of soft computing in CBR is briefly described in section five. The final section gives some examples of successful CBR applications in different areas. 1 What is Case-Based Reasoning? 1.1

this paper we discuss two facets of such systems. First we discuss the relationship between CBR and IR in this context and second we show how a classifying terminological reasoner can be used for implementing such a system.