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.

Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in...

Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criteria are learned. This paper examines the issues involved in modeling the learning of memory search strategies. It discusses the general requirements for appropriate strategy learning and presents a model of memory search strategy learning applied to the problem of retrieving relevant information for adapting cases in case-based reasoning. It discusses an implementation of that model, and, based on the lessons learned from that implementation, points towards issues and directions in refining the model. Introduction Much recent AI research on memory focuses on analyzing the indices that are relevant to particular classes of retrieval problems (e.g., (Domeshek, 1992; Leake, 1992; Owens, 1991)). The problem of how memory search strategies can be learned...

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...

Competing theories of analogical reasoning have disagreed on the relative contributions of surface and structural features to the access of previously read base stories when one is reading a current cue story. A key limitation of the prior work was that surface and structural feature overlap between bases and cues was not manipulated precisely. The present study systematically manipulated the number of surface and structural matches to determine their relative effect on access. Results involving reminding and readingtime measures suggest that surface and lower-order structural features affected access about equally, at least when a higher-order relation (HOR) was shared between a base and cue story. When a HOR was not shared, surface feature overlap continued to affect access while lower-order structural features had a less reliable effect. Models of access might need to be adjusted to account for these phenomena. We are often reminded of a prior problem or example when we are working on a current problem or just learning about a new situation. However, the problem or example that comes to mind might or might not be all that relevant. The factors that influence which prior cases one is reminded of have been of interest to a variety of researchers, particularly those who study analogical

Research has shown that the activation and application of a significant-other representation to a new person, or transference, occurs in everyday social perception (S. M. Andersen & A. Baum, 1994; S. M. Andersen & S. W. Cole, 1990). Using a combined idiographic and nomothetic experimental paradigm, two studies examined the role of chronic accessibility of significant-other representations in transference. After learning about 4 fictional people, 1 of whom resembled a significant other, participants ' recognition memory was assessed. Both studies showed greater false-positive memory in the significant-other condition, relative to control, even in the absence of priming. Study 2 showed that although the effect was greater when the significant-other representation was concretely applicable to the target information, it occurred even when no such applicability was present. Results implicate the chronic accessibility of significant-other representations in transference. Mental representations of significant others serve as storehouses of information about important individuals from one's life. Interestingly, these representations can also be triggered by a new person and applied to this person in the context of everyday interpersonal relations (Andersen & Baum, 1994; Andersen

ith different meaning, dependingon their content andthe context in which they are used. People may then have nearly asmany selvesas they have significant interpersonal relationships(Sullivan, 1953; see also Kelly, 1955), providing for both contextual variability andthe longstandingrepresentationsasa chronic influence. We assess idiosyncratic knowledge representations in memory and track their influence on affect and mot ivat ion . We also examine how self-regulato ry p rocesses furt her modulat e t hese responses. Our conceptualization focuseson the ways the self is linked to other people who are (or hadbeen) significant, who have hadan impact on one s life, and in whom one is (or once was) emotionally invested. Because mental representat ions of significant others and t heir relat ional linkages to the self are central in t he model, t he emotional investmentsone has in significant othersplay a role in determiningone sresponses, includingone s self-regulato ry effo r ts. One has a rel

Standard methods for abductive understanding are neutral to prior experience and current goals. Candidate explanations are built from scratch by backwards chaining, without considering how similar situations were previously explained, and selection of the candidate to accept is based on its likelihood, without considering the information needs beyond routine understanding. Problems arise when applying these methods to everyday understanding: The vast range of possible explanations makes it difficult to control the cost of explanation construction and to assure that the explanations generated will actually be useful. We argue that these problems can be overcome by using goals and experience to guide both explanation generation and evaluation. Our work is within the framework of case-based explanation, which builds explanations by retrieving and adapting prior explanations stored in memory. We substantiate our model by describing mechanisms that enable it to effectively generate good exp...

Despite the progress made in the field of intelligent tutoring systems (ITS), it is still a major challenge to build systems that can teach about complex, ill-structured domains. A chief reason is that detailed, dynamic modeling of students ' knowledge is intractable in such areas, and complete, correct models of expert knowledge are inherently difficult to build. These difficulties have led some to argue that the goal of intelligent tutoring should be abandoned and that more benefit could be provided by systems without tutoring. We believe that there are many areas in which tutorial intervention is essential, particularly for the communication of expertise. In this paper we advocate basing tutorial intervention on an analysis of a student's likely points of interest within a learning environment, rather than on his or her state of knowledge. This interestfocused approach results in considerable simplification of the modeling task, and has other advantages as well. We describe an interest-tracing intelligent tutoring framework that we have been using to build learning environments for such ill-structured tasks as selling, managing, and other interpersonal skills using tutorial guidance. Our design is based on case-based reasoning as a model of human problem-solving. Expertise is modeled as an organized library of cases; student modeling is restricted to the considerations that enter into the decision to retrieve and present relevant cases. This paper describes the cognitive theory underlying our tutoring approach, and the implementation of the tutor. We show how it is possible to present useful tutorial intervention based on a student's state of interest, without an overwhelming burden of student modeling.