This work is the second part of a general similarity-based theory of case-based reasoning (CBR), which moves CBR towards a firm theoretical foundation based on similarity-based reasoning. This paper will first examine abductive CBR and deductive CBR and propose a knowledge-based model for integrating abductive CBR and deductive CBR. It then proposes similarity-based models for rule-based and fu...

In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR...

Case-Based Reasoning (CBR) is an Artificial Intelligence approach to learning and problem solving based on past experience. CBR combines aspects from the knowledge-based systems as well as from the machine learning field. In this chapter we present the main characteristics of CBR technology, describe the main CBR subtasks and methods, and the main underlying principles. CBR is presented more fr...

Maintenance of Case-Based Reasoning (CBR) systems became an important area since applications of CBR technologies were established in different real-world domains. Maintenance issues cover all aspects that help to keep a running CBR system in a usable state of high quality. Concepts and techniques that were developed for maintenance of CBR systems range from methodologies and frameworks that pa...

This paper will examine new issues resulting from applying CBR in e-commerce and propose a unified logical model for CBR-based e-commerce systems (CECS) which consists of three cycles and covers almost all activities of applying CBR in e-commerce. This paper also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptat...

In this paper we present CAT-CBR a component-based platform for developing CBR systems. CAT-CBR uses UPML (Universal Problemsolving Methods Language) for specifying CBR components. A collection of CBR components for retrieval of propositional cases is presented in detail. The CAT-CBR platform guides the engineer using a case-based recommendations system to develop a configuration of components ...

Industrial diagnosis is a domain where problems are recurrent and therefore previous documented solution can be successfully reused. Different methodologies may be implemented for a given diagnosis domain, one of the appropriate, appears to be Case-Based Reasoning (CBR). A CBR system is a combination of processes and knowledge called “knowledge containers“, that allows to preserve and to exploi...

Case-based Reasoning (CBR) is an Artificial Intelligence (AI) paradigm that attempts to solve new problems based on its past experiences of solving similar problems. Due to the intrinsic similarity of CBR with human reasoning process, it is used for automated problem-solving. The effectiveness of CBR can be enhanced by combining it with other AI techniques. One such approach is the inclusion of...

rent work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characteri...