Case-Based Reasoning: A Concise Introduction (Synthesis Lectures on Artificial Intelligence and Machine Learning)

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. Cover Copyright Page Title Page Dedication Contents Preface Acknowledgments Introduction CBR Systems Taxonomy Foundational Issues Related Fields Bibliographic Notes The Case-Base Vocabulary Case Modeling Problem Description Solution Description Outcome Case-Base Organization Bibliographic Notes Reasoning and Decision Making Retrieve Similarity Assessment Ranking and Selection Normalization, Discretization, and Missing Data Reuse Solution Copy Solution Adaptation Specific Purpose Methods Revise Bibliographic Notes Learning Similarity Learning Measure Learning Feature Relevance Learning Maintenance Retain Review Restore Bibliographic Notes Formal Aspects Description Logics Bayesian Model Fuzzy Set Formalization Probabilistic Formalization Case-Based Decisions Bibliographic Notes Summary and Beyond Explanations Provenance Distributed Approaches Bibliographic Notes Bibliography Author's Biography Presents a concise introduction to case-based reasoning, providing the essential building blocks for the design of case-based reasoning systems, as well as bringing together the main research lines in this field to encourage students to solve current CBR challenges.