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 this work, we are interested in how rational decision makers reason with and about reasons in a domain, practical ethics, where they appear to reason about reasons symbolically in terms of both abstract moral principles and case comparisons. The challenge for reasoners, human and artificial, is to use abstract knowledge of reasons and principles to inform decisions about the salience of similarities and differences among cases while still accounting for a case's or problem's specific contextual circumstances. TRUTH-TELLER is a program we have developed and tested that compares pairs of cases presenting ethical dilemmas about whether to tell the truth. The program's methods for reasoning about reasons help it to make context sensitive assessments of the salience of similarities and differences.

Abduction is often viewed as inference to the "best" explanation. However, the evaluation of the goodness of candidate hypotheses remains an open problem. Most artificial intelligence research addressing this problem has concentrated on syntactic criteria, applied uniformly regardless of the explainer's intended use for the explanation. We demonstrate that syntactic approaches are insufficient to capture important differences in explanations, and propose instead that choice of the "best" explanation should be based on explanations' utility for the explainer 's purpose. We describe two classes of goals motivating explanation: knowledge goals reflecting internal desires for information, and goals to accomplish tasks in the external world. We describe how these goals impose requirements on explanations, and discuss how we apply those requirements to evaluate hypotheses in two computer story understanding systems. In order to learn from experience, a reasoner must be able to explain what...

In case-based reasoning systems, the case adaptation process is traditionally controlled by static libraries of hand-coded adaptation rules. This paper proposes a method for learning adaptation knowledge in the form of adaptation strategies of the type developed and hand-coded by Kass [90] . Adaptation strategies differ from standard adaptation rules in that they encode general memory search procedures for finding the information needed during case adaptation; this paper focuses on the issues involved in learning memory search procedures to form the basis of new adaptation strategies. It proposes a method that starts with a small library of abstract adaptation rules and uses introspective reasoning about the system's memory organization to generate the memory search plans needed to apply those rules. The search plans are then packaged with the original abstract rules to form new adaptation strategies for future use. This process allows a CBR system not only to learn about its domain, b...

Although creativity has largely been studied in problem solving contexts, creativity consists of both a generative component and a comprehension component. In particular, creativity is an essential part of reading and understanding of natural language stories. We have formalized the understanding process and have developed an algorithm capable of producing creative understanding behavior. We have also created a novel knowledge organization scheme to assist the process. Our model of creativity is implemented as a portion of the ISAAC (Integrated Story Analysis And Creativity) reading system, a system which models the creative reading of science fiction stories. Introduction Creativity remains a largely unexplained facet of human intelligence; neither psychologists nor artificial intelligence researchers have produced complete theories of it. While most creativity researchers have investigated the behavior in a problem solving context, we are more interested in how creativity is manifes...

Reasoning from prior cases or abstractions requires that a system identify relevant similarities between the current situation and objects represented in memory. Often, relevance depends upon abstract, thematic, costly-to-infer properties of the situation. Because of the cost of inference, a case retrieval system needs to learn which descriptions are worth inferring, and how costly that inference will be. This paper outlines the properties that make an abstract thematic feature valuable to a case-based reasoner, and recasts the problem of case retrieval into a framework under which a system can explicitly and dynamically reason about the cost of acquiring features relative to their information value. 1 Retrieval, description, and learning For a case-based reasoner to make effective use of recalled prior experiences, it must be able to judge which of its cases are applicable to the current situation. This problem is not new nor is it unique to case-based reasoning: any system that re...

In order to learn effectively, a system must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires a declarative representation of the reasoning performed by the system during the performance task. This paper presents a taxonomy of possible reasoning failures that can occur during this task, their declarative representations, and their associations with particular learning strategies. We propose a theory of Meta-XPs, which are explanation structures that help the system identify failure types and choose appropriate learning strategies in order to avoid similar mistakes in the future. A program called Meta-AQUA embodies the theory and processes examples in the domain of drug smuggling. Keywords: Introspective learning, metareasoning, explanation patterns. 1 Introducti...

explanation schemas for why people do things. These are standard high-level explanations for actions, such as "Actor does action because the outcome of action satisfies a goal of the actor." 2. Explanatory cases. These are specific explanations for particular situations, such as "Shiite Moslem religious fanatic goes on suicide bombing mission." For example, an explanation of type 1 for story S-2 might be "Because she wanted to destroy the Israeli base more than she wanted to stay alive." An explanation of type 2 would be simply "Because she was a religious fanatic." The internal causal structure of the latter explanation could then be elaborated to provide a detailed motivational analysis in terms of explanations of the first type if necessary. Both types of explanatory knowledge are represented using volitional XPs with the internal structure discussed earlier. Volitional XPs relate the actions in which the characters in a story are involved to the outcomes that those actions had for ...

states (as i ) point to bins (bold lines), containing world states (s j ). World states in turn point (dashed lines) to past plans (P ) in which they are contained.

TRUTH-TELLER, a program for testing a Case-Based Reasoning (CBR) knowledge representation in practical ethics, compares cases presenting ethical dilemmas about whether to tell the truth. Its comparisons list ethically relevant similarities and differences (i.e., reasons for telling or not telling the truth which apply to both cases, and reasons which apply more strongly in one case than another or which apply only to one case). The reasons may invoke ethical principles or selfish considerations. We describe a knowledge representation for this practical ethical domain including representations for reasons and principles, truth telling episodes, contextually important scenarios, and comparison rules. In a preliminary evaluation, a professional ethicist scored the program's output for randomly-selected pairs of cases. The work contributes to AI CBR efforts to integrate general principles and context-sensitive information in symbolically assessing case similarity and to model co...