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.

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as online sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (Self-Improving Navigation System). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addr...

A fundamental issue in case-based reasoning is similarity assessment: determining similarities and differences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the cases already in memory. However, the success of such methods depends on the input case description being sufficiently complete to reflect the important features of the new situation, which is not assured. In case-based explanation of anomalous events during story understanding, the anomaly arises because the current situation is incompletely understood; consequently, similarity assessment based on matches between known current features and old cases is likely to fail because of gaps in the current case's description. Our solution to the problem of gaps in a new case's description is an approach that we call constructive similarity assessment. Constructive similarity assessment treats similarity assessment not as a simple comparison between fixed new and old cases, b...

{shimazu,akihiro} joke.cl.nec.co.jp kitano spls26.ccs.mt.nec.co.jp Vital information for corporate activities is generally stored in large databases. While conventional data-base management systems offer limited query flexibility, systems capable of generating similarity-based queries, such as those seen in case-based reasoning research, would certainly enhance the utility of data resources. This paper describes a method for building case-based systems using a conventional relational data-base (RDB). The core of the algorithm is a novel approach to similarity computing in which database query form similarities, rather than similarities of individual cases, are computed. The method uses Standard Query Language (SQL) to achieve nearest neighbor matching, thus allowing similarity-based database retrieval. It has been implemented as a part of the CARET case retrieval tool and evaluated through the use of a newly developed corporate-wide case-based system for a software quality control domain. Experiments have shown the proposed method to provide retrieval results equivalent to those of non-RDB implementation at a sufficiently fast response time. 1

Most legal expert systems attempt to implement complex models of legal reasoning. Yet the utility of a legal expert system lies not in the extent to which it simulates a lawyer's approach to a legal problem, but in the quality of its predictions and of its arguments. A complex model of legal reasoning is not necessary: a successful legal expert system can be based upon a simplified model of legal reasoning. Some researchers have based their systems upon a jurisprudential approach to the law, yet lawyers are patently able to operate without any jurisprudential insight. A useful legal expert system should be capable of producing advice similar to that which one might get from a lawyer, so it should operate at the same pragmatic level of abstraction as does a lawyer—not at the more philosophical level of jurisprudence. A legal expert system called SHYSTER has been developed to demonstrate that a useful legal expert system can be based upon a pragmatic approach to the law. SHYSTER has a simple representation structure which simplifies the problem of knowledge acquisition. Yet this structure is complex enough for SHYSTER to produce useful advice. SHYSTER is a case-based legal expert system (although it has been designed so that it can be linked with a rule-based system to form a hybrid legal expert system). Its advice is based upon an examination of, and an argument about, the similarities and differences between cases. SHYSTER attempts to model the way in which lawyers argue with cases, but it does not attempt to model the way in which lawyers decide which cases to use in those arguments. Instead, it employs statistical techniques to quantify the similarity between cases. It decides which cases to use in argument, and what prediction it will make, on the basis of that similarity measure. SHYSTER is of a general design: it provides advice in areas of case law that have been specified by a legal expert using a specification language. Four different, and disparate, areas of law have been specified for SHYSTER, and its operation has been tested in each of those legal domains. Testing of SHYSTER in these four domains indicates that it is exceptionally good at predicting results, and fairly good at choosing cases with which to construct its arguments. SHYSTER demonstrates the viability of a pragmatic approach to legal expert system design.

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...

Most work in artificial intelligence and law has concentrated on modelling the type of reasoning done by trial lawyers. In fact, most lawyers' work involves planning -- for example, wills and trusts, real estate deals, and business mergers and acquisitions. Certain planning issues, such as the use of underspecified, or "open-textured" rules, are illustrated especially clearly in this domain. In this thesis, I set forth the characteristic features of planning in law, place it in the context of past artificial intelligence work in both law and planning, and describe CHIRON, a system that I have developed implementing my theory of open-textured planning in the domain of personal income tax law.

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...

HYPO is a case-based reasoning system that evaluates problems by comparing and contrasting them with cases from its Case Knowledge Base (CICB). It generates legal arguments citing the past cases as justifications for legal conclusions about who should win in problem disputes involving trade secret law. HYPO’s arguments present competing adversarial views of the problem and it poses hypotheticals to alter the balance of the evaluation. HYPO uses Dimensions as a generalization scheme for accessing and evaluating cases. HYPO’s reasoning process and various computational definitions are described and illustrated, including its definitions for computing relevant similarities and differences, the most on point and best cases to cite, four kinds of counter-examples, targets for hypotheticals and the aspects of a case that are salient in various argument roles. These definitions enable HYPO to make contextually sensitive assessments of relevance and salience without relying on either a strong domain theory or a priori weighting schemes. 1.

When solving a complex problem, gathering relevant information to understand the situation and imposing appropriate interpretations on that information are critical to problem solving success. These two tasks are especially difficult in weak-theory domains -- domains in which knowledge is incomplete, uncertain, and contradictory. In such domains, experts may rely on experience for all aspects of problem solving. We have developed a case-based approach to problem elaboration and interpretation in such domains. An experience-based problem-solver should be able to incrementally acquire information and, in the course of that acquisition, be reminded of multiple cases in order to present multiple viewpoints to problems that present multiple faults. We are addressing issues of 1) elaboration and interpretation of complex problem situations; 2) multiple interpretations; and 3) the role of categories as the foci of reasoning in the context of the Organizational Change Advisor (ORCA). Its model...