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, but as a process for deciding which types of features should be investigated in the new situation and, if the features are borne out by other knowledge, added to the description of the current case. Constructive similarity assessment does not merely compare new cases to old: using prior cases as its guide, it dynamically carves augmented descriptions of new cases out of memory.

AI research on explanation has not seriously addressed the influence of explainer goals on explanation construction. Likewise, psychological research has tended to assume that people's choice between explanations can be understood without considering the explainer's task. W e take the opposite view: that the influence of task is central to judging explanations. Explanations serve a wide range of tasks, each placing distinct requirements on what is needed in an explanation. W e identify eight main classes of reasons for explaining novel events, and show how each one imposes requirements on the information needed from an explanation. These requirements form the basis of dynamic, goal-based explanation evaluation implemented in the program ACCEPTER . We argue that goal-based evaluation of explanations offers three important advantages over static criteria: First, it gives a way for an explainer to know what to elaborate if an explanation is inadequate. Second, it allows cross-contextual use of explanations, by deciding when an explanation built in one context can be applied to another. Finally, it allows explainers to make a principled decision of when to accept incomplete explanations without further elaboration, allowing explainers to conserve processing resources, and also to deal with situations they can only partially explain.

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.

mputer 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 CB R as a cognitive model is how case adaptation knowledge is learned. W e 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 successftil rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. Whe n 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 memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning

We present a mechanism for remembering explanations and re-using them to explain newepisodes. This task requires a representation scheme for explanations, a dynamically organizedmemory, and a means of modifying old explanations to fit new facts. In this paper we focus onmemory organization. W e describe strategies for indexing and retrieving explanations, for usingcausal knowledge to select relevant features of episodes and for guiding generalization. W e discusswork in progress on a computer implementation of this model.

The case-based reasoning (CBR) process solves problems by retrieving prior solutions and adapting them to fit new circumstances. Many studies examine how casebased reasoners learn by storing new cases and refining the indices used to retrieve cases. However, little attention has been given to learning to refine the process for applying retrieved cases. This paper describes research investigating how a case-based reasoner can learn strategies for adapting prior cases to fit new situations, and how its similarity criteria may be refined pragmatically to reflect new capabilities for case adaptation. We begin by highlighting psychological research on the development of similarity criteria and summarizing our model of case adaptation learning. We then discuss initial steps towards pragmatically refining similarity criteria based on experiences with case adaptation.