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.

... this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -- the Adaptive Place Advisor -- treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

We have studied the problem of generating expressive musical performances in the context of tenor saxophone interpretations. We have done several recordings of a tenor sax playing different Jazz ballads with different degrees of expressiveness including an inexpressive interpretation of each ballad. These recordings are analyzed, using SMS spectral modeling techniques, to extract information related to several expressive parameters. This set of parameters and the scores constitute the set of cases (examples) of a case-based system. From this set of cases, the system infers a set of possible expressive transformations for a given new phrase applying similarity criteria, based on background musical knowledge, between this new phrase and the set of cases. Finally, SaxEx applies the inferred expressive transformations to the new phrase using the synthesis capabilities of SMS. 1 Introduction We have developed SaxEx, a case-based reasoning system for generating expressive performances of me...

One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case’s adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional, similarity assumption in an effort to deliver more sophisticated and scaleable reasoning systems. Smyth & Keane 3 Adaptation-Guided Retrieval 1

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

Collaborative Decision Making problems can be addressed through argumentative discourse and collaboration among the users involved. Consensus is achieved through the process of collaboratively considering alternative understandings of the problem, competing interests, priorities and constraints. The application of formal modeling and analysis tools to solve the related processes is impossible before the problem can be articulated in a concise and agreed upon manner. This paper describes HERMES, a system that augments classical decision making approaches by supporting argumentative discourse among decision makers. It is fully implemented in Java and runs on the Web, thus providing relatively inexpensive access to a broad public. Using an illustrative example, we present the argumentation elements, discourse acts and reasoning mechanisms involved in HERMES. We also describe the integration of advanced features to the system; these enable users to retrieve data stored in remote da...

ion in Case-Based Reasoning Ralph Bergmann and Wolfgang Wilke University of Kaiserslautern, Centre for Learning Systems and Applications (LSA) Dept. of Computer Science, P.O.-Box 3049, D-67653 Kaiserslautern, Germany E-Mail: fbergmann,wilkeg@informatik.uni-kl.de Abstract. This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We argue that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation....

This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. This distance can be used for classification via a k-nearest-neighbor process. Experiments on the mutagenesis dataset validate the approach, in terms of predictive accuracy, computational cost, and robustness with respect to the parameters of the algorithm.

Stratified case-based reasoning is a technique in which abstract solutions produced during hierarchical problem solving are used to assist casebased retrieval, matching, and adaptation. We describe the motivation for the integration of case-based reasoning with hierarchical problem solving, exemplify its benefits, detail a set of algorithms that implement our approach, and present their comparative empirical evaluation on a path planning task. Our results show that stratified case-based reasoning significantly decreases the computational expense required to retrieve, match, and adapt cases, leading to performance superior both to simple case-based

Abstract. While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time games. Focusing on WARGUS, they report good results for a genetic algorithm that searches in plan space, and for a weighting algorithm (dynamic scripting) that biases subplan retrieval. However, both approaches assume a static opponent, and were not designed to transfer their learned knowledge to opponents with substantially different strategies. We introduce a plan retrieval algorithm that, by using three key sources of domain knowledge, removes the assumption of a static opponent. Our experiments show that its implementation in the Case-based Tactician (CAT) significantly outperforms the best among a set of genetically evolved plans when tested against random WARGUS opponents. CAT communicates with WARGUS through TIELT, a testbed for integrating and evaluating decision systems with simulators. This is the first application of TIELT. We describe this application, our lessons learned, and our motivations for future work. 1