Case-Based Reasoning

Prof. Michael M. Richter completed his PhD on mathematical logic at the University of Freiburg, and his Habilitation in mathematics at the University of Tübingen. He taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships. He was president of the German Society for Mathematical Logic and the Foundations of Exact Sciences for four years. Most recently, from 1986 he held a chair in computer science at the University of Kaiserslautern, where he was also a founding scientific director of the DFKI (German Research Center for Artificial Intelligence). In 2005 he became an adjunct professor at the University of Calgary. He has taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Prof. Richter is one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and he demonstrated the real-world viability of the approach with successful commercial products. Dr. Rosina Weber is an Associate Professor in the College of Information Science and Technology at Drexel University in Philadelphia. She received her PhD from the Dept. of Production Engineering of the Federal University of Santa Catarina in Brazil, in a "sandwich” program collaboration with the Dept. of Industrial & Management Systems Engineering of the University of South Florida in Tampa. She has a master’s degree in operations research and a bachelor’s degree in business administration. She was a postdoc at the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Lab in Washington, DC. Her key research interest is knowledge engineering and management, using intelligent methods and techniques such as case-based reasoning, information extraction, fuzzy sets, genetic algorithms, neural networks, and ontologies in applications in domains such as law, defense, nutrition, medicine, finance, biology, and psychology.

• Part I - Basics and Preliminaries.- Chap. 1 - Introduction.- Chap. 2 - Basic CBR Elements.- Chap. 3 - Extended View.- Chap. 4 - Application Examples.- Part II - Core Methods.- Chap. 5 - Case Representations.- Chap. 6 - Basic Similarity Topics.- Chap. 7 - Complex Similarity Topics.- Chap. 8 - Retrieval.- Chap. 9 - Adaptation.- Chap. 10 - Evaluation, Revisions, and Learning.- Chap. 11 - Development and Maintenance.- Part III - Advanced Elements.- Chap. 12 - Advanced CBR Elements.- Chap. 13 - Advanced Similarity Topics.- Chap. 14 - Advanced Retrieval.- Chap. 15 - Uncertainty.- Chap. 16 - Probabilities.- Part IV - Complex Knowledge Sources.- Chap. 17 - Textual CBR.- Chap. 18 - Images.- Chap. 19 - Sensor Data and Speech.- Chap. 20 - Conversational CBR.- Chap. 21 - Knowledge Management.- Part V - Appendices.- Chap. 22 - Basic Formal Definitions and Methods.- Chap. 23 - Relations and Comparisons with Other Techniques.