A breakthrough on today’s fastest growing artificial intelligence technique
Many of today’s engineering and business computer applications require decisions to be made on the basis of uncertain or incomplete information, a phenomenon that has resulted in the development of case-based reasoning, a powerful computing technique by which a system’s problem-solving ability is enhanced by reference to its previously stored experiences.

Foundations of Soft Case-Based Reasoning is the first book of its kind to provide a unified framework for understanding how soft computing techniques can be used to build and maintain case-based reasoning (CBR) systems. Written by two internationally renowned experts, the book demonstrates the latest advances of machine learning and intelligence and presents CBR methodologies and algorithms designed to be useful for both students of artificial intelligence and practitioners in the field.

Structured according to the four major phases of the problem-solving process of a CBR system: representation and indexing of cases, case selection and retrieval, case adaptation, and case-base maintenance; the authors provide a solid foundation of the subject with a balanced mix of theory, algorithms, and application.

An important resource for students and practitioners alike, Foundations of Soft Case-Based Reasoning:

* Illustrates decision-making problems in a wide range of engineering and business applications, such as data mining and pattern recognition
* Features real-world examples of successful applications in such areas as medical diagnosis, weather prediction, law interpretation, and Web access path determination, to name a few
* Describes in a unified way how the merits of various soft computing tools (e.g., fuzzy sets, neural networks, genetic algorithms, and rough sets) can give rise to more efficient CBR systems
* Demonstrates the significance of granular computing and rough sets in CBR