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.

Combining the predictions of a set of classifiers has been shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. Increased accuracy has been shown in a variety of real-world applications, ranging from protein sequence identification to determining the fat content of ground meat. Despite such individual successes, the answers are not known to fundamental questions about classifier combination, such as "Can classifiers from any given model class be combined to create a composite classifier with higher accuracy?" or "Is it possible to increase the accuracy of a given classifier by combining its predictions with those of only a small number o...

. Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to be expanded as necessary to describe new cases. Such representations can be implemented as, for example, semantic networks or lists of concrete propositions in some logic. We believe that graph-structured representations offer significant advantages, and thus we are investigating ways to implement such representations efficiently. We make a "case-based argument" using examples from two systems, chiron and caper, to show how a graph-structured representation supports two different kinds of case-based planning in two different domains. We discuss the costs associated with graph-structured represe...

In case-based reasoning (CBR), graph-structured representations are desirable for complex application domains such as planning and design. Graph is a powerful data structure and allows knowledge to be encoded completely and expressively. However, the advantages come with a computational overhead for case retrieval, which presently prevents the usage of graph-structured representation for large-scale problems. In this paper, we describe a two-stage strategy that is based on the tabu search for solving the graph-structured case retrieval. The experiments were performed on synthetically generated graphs. The preliminary results obtained show the effectiveness of our approach.

Abstract. Case-based Reasoning (CBR) began as a theory of human cognition, but has attracted relatively little direct experimental or theoretical investigation in psychology. However, psychologists have developed a range of instance-based theories of cognition and have extensively studied how similarity to past cases can guide categorization of new cases. This paper considers the relation between CBR and psychological research, focussing on similarity in human and artificial case-based reasoning in law. We argue that CBR, psychology and legal theory have complementary contributions to understanding similarity, and describe what each offers. This allows us to establish criteria for assessing existing CBR systems in law and to establish what we consider to be the crucial goals for further research on similarity, both from a psychological and a CBR perspective.

Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. As a result, case representation is critical: an incomplete case representation limits the system's reasoning power. In this paper we argue for structure-based case representations, which express arbitrary relations among objects in a flexible way, over more limited or inflexible methods. We motivate the distinction between these kinds of representations with examples from information retrieval systems, CBR systems, and computational models of human analogical reasoning. Structure-based representations provide the benefits of greater expressivity and economy. We give examples of these benefits from two case-based planning systems we have developed, CaPER and CHIRON, and show how the case matching and case acquisition costs can be reduced through the use of massively parallel techniques. This paper is being submitted as a scientific paper. K...

In case-based reasoning (CBR), graph-structured representations are desirable for complex application domains such as planning and design. Graph is a powerful data structure and allows knowledge to be encoded completely and expressively. However, the advantages come with a computational overhead for case retrieval, which presently prevents the usage of graph-structured representation for large-scale problems. In this paper, we describe a two-stage strategy that is based on the tabu search for solving the graph-structured case retrieval. The experiments were performed on synthetically generated graphs. The preliminary results obtained show the effectiveness of our approach.

Most case-based reasoning systems have used a single "best " or "most similar " case as the basis for a solution. For many problems, however, there is no single exact solution. Rather, there is a range of acceptable answers. We use cases not only as a basis for a solution, but also to indicate the boundaries within which a solution can be found. We solve problems by choosing some point within those boundaries. In this paper, I discuss this use of cases with illustrations from CHIRON, a system T have implemented in the domain of personal income tax planning. 1