Case Based Approximate Reasoning

Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR. The case-based approximate reasoning methods thus obtained especially emphasize the heuristic nature of case-based inference and aspects of uncertainty in CBR.

This book presents case-based reasoning in a systematic approach with two goals: to present rigorous and formally valid structures for precise case-based reasoning, and to demonstrate the range of techniques, methods, and tools available for many applications.

This book constitutes the refereed proceedings of the 5th European Workshop on Case-Based Reasonning, EWCBR 2000, held in Trento, Italy in September 2000. The 40 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. All curves issues in case-based reasoning, ranging from foundational and theoretical aspects to advanced applications in various fields are addressed.

This book constitutes the refereed proceedings of the Second International Conference on Case-Based Reasoning, ICCBR-97, held in Providence, RI, USA, in July 1997. The volume presents 39 revised full scientific papers selected from a total of 102 submissions; also included are 20 revised application papers. Among the topics covered are representation and formalization, indexing and retrieval, adaptation, learning, integrated approaches, creative reasoning, CBR and uncertainty. This collection of papers is a comprehensive documentation of the state of the art in CBR research and development.

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.

Case-based reasoning paradigms offer automatic reasoning capabilities which are useful for the implementation of human like machines in a limited sense. This research book is the second volume in a series devoted to presenting Case-based reasoning (CBR) applications. The first volume, published in 2010, testified the flexibility of CBR, and its applicability in all those fields where experiential knowledge is available. This second volume further witnesses the heterogeneity of the domains in which CBR can be exploited, but also reveals some common directions that are clearly emerging in recent years. This book will prove useful to the application engineers, scientists, professors and students who wish to develop successful case-based reasoning applications.

This book constitutes the refereed proceedings of the 19th International Conference on Case-Based Reasoning, held in London, UK, in September 2011. The 32 contributions presented together with 3 invited talks were carefully reviewd and selected from 67 submissions. The presentations and posters covered a wide range of CBR topics of interest both to practitioners and researchers, including CBR methodology covering case representation, similarity, retrieval, and adaptation; provenance and maintenance; recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning systems.

Documents realistic applications of approximate reasoning techniques, with emphasis placed on operational systems. The papers presented explore new areas of practical decision-making and control systems by considering important aspects of fuzzy logic theory and the latest developments in the field of expert systems. Specific fields of application covered include modelling and control, management, planning, diagnostics, finance and software. Contains 12 papers.