Case-Based Reasoning: A Concise Introduction (Synthesis Lectures on Artificial Intelligence and Machine Learning)
暫譯: 案例推理：簡明介紹（人工智慧與機器學習綜合講座）

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges. Table of Contents: Introduction / The Case-Base / Reasoning and Decision Making / Learning / Formal Aspects / Summary and Beyond