Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.
Clustering and Classification of Manufacturing Enterprises Regarding Their Industry 4.0 Reshoring Incentives
