In case-based reasoning, previous reasoning experiences are used directly to solve new problems and make Inferences,rather than doing those tasks from saatch using generalized methods. One major advantage of a case-based approach is that It can help a reasoner avoid repeating previously-made mistakes. W h e n the case-based reasoner Is reminded of a case in which a mistake was made. It provides a warning of the potential for a mistake. If the previous case was finally solved successfully, it can provide a suggestion of what to do instead. In this paper, w e describe the process by which a case-based reasoner can take advantage of previous failures. W e Illustrate with cases from the domains of common-sense mediation and menu planning and show a program called JULIA reasoning In the domain of menu planning.

The most important support process a case-based reasoner needs is a memory for cases. Among its functions, the memory for cases must be able to select out the most appropriate cases for the case-based reasoner to use at any time. In this paper, we present the selection processes implemented in PARADYME , a case memory designed to work alongside a case-based reasoner.PARADYME has a two-step retrieval process. In the first step, it retrieves the set of partial matches from the memory. In the second, it selects out a small set of "best" matches. PARADYME chooses"best" cases using a set of six preference heuristics: goal-directed preference, salience,specificity, frequency, recency, and ease of adaptation. PARADYME is novel in two ways. Its use of preferences for choosing a best case means that its principles act as selectors rather than restrictors. And its emphasis in choosing best cases is on usefulness rather than similarity.

One of the major activities creative problem solvers engage in is exploration and evaluation of alternatives, often adapting and merging several possibilities to create a solution to the new problem. We propose a process that models this activity and discuss the requirements it puts on representations and reasoning processes and present a program that solves problems by following this procedure.

Problem solving Is known to vary in some predictable ways as a function of experience. In this study, we have Investigated the effects of experience on the problem solving behavior and knowledge base of workers In an applied setting: automobile mechanics. The automobile itself is a highly complex system with many interconnected subsystems.Problem descriptions (i.e., symptoms) presented to a mechanic who needs to diagnose a car, however, are usually quite sketchy, requiring the collection of more information before solution. Novices are less able than experts to diagnose any but the obvious problems, and w e are Interested in Identifying the qualitative differences between mechanics at different levels of expertise. In the study reported, w e observed three student mechanics in a postsecondary technical school, each at a different level of expertise, diagnose six problems Introduced Into cars In the school. W e then analyzed the protocols w e collected to find the knowledge and strategies used in solving each problem.W e also analyzed the series of protocols for each student to find the changes in knowledge and strategies used In solving later problems as compared to earlier problems. Differences were seen in both the knowledge used by the subjects and in their general approach to diagnosis. As a result of experience, the student mechanics seemed to Improve In three areas: (1) their knowledge of the relationships between symptoms and possible failures was augmented,(2) their causal models of the car's systems were augmented, and (3) their general troubleshooting procedures and decision rules were much Improved.

The phenomena of reminding has been receiving quite a bit of attention in the past few years. Researchers have been looking at how previous experience can help in understanding and problem solving. As much of this work has shown, reminding is a complicated process. In this paper, we present requirements on a cognitive architecture that can promote and support these observations. The architectural requirements are those needed by a machine that can use experience to reason. To motivate these requirements, we present observations of reminding that are based on analyses of both actual remindings and hypothetical cases.