From the Publisher:
"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. 
In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. 
Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Foundations of neural networks, fuzzy systems, and knowledge engineering

This paper compares five methods for pruning decision trees, developed from sets of examples. When used with uncertain rather than deterministic data, decision-tree induction involves three main stages—creating a complete tree able to classify all the training examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper concerns the second stage—pruning. It presents empirical comparisons of the five methods across several domains. The results show that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems. They also show that there is no significant interaction between the creation and pruning methods.

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An Empirical Comparison of Pruning Methods for Decision Tree Induction

Preface Acknowledgements 1. Values, history and perception 2. Kinds of uncertainty 3. Conventions and the risk management cycle 4. Experts, stakeholders and elicitation 5. Conceptual models and hazard assessment 6. Risk ranking 7. Ecotoxicology 8. Logic trees and decisions 9. Defining and eliciting intervals 10. Monte Carlo 11. Inference, decisions, monitoring and updating 12. Decisions and risk management References Index.

Risks and decisions for conservation and environmental management

Information on knowledge elicitation methods is widely scattered across the fields of psychology, business management, education, counseling, cognitive science, linguistics, philosophy, knowledge engineering and anthropology. The purpose of this review is to (1) identify knowledge elicitation techniques and the associated bibliographic information, (2) organize the techniques into categories on the basis of methodological similarity, and (3) summarize for each category of techniques strengths, weaknesses, and recommended applications. The review is intended to provide a starting point for those interested in applying or developing knowledge elicitation techniques, as well as for those more generally interested in exploring the scope of the available methodology.

Varieties of knowledge elicitation techniques

Recent studies have confirmed the importance of understanding the cognition of users and information systems (IS) professionals. These works agree that organizational cognition is far too critical to be ignored as it can impact on IS outcomes. While cognition has been considered in a variety of IS contexts, no specific methodology has dominated. A theory and method suitable to the study of cognition-defined as personal constructs that individuals use to understand IT in organizations-is Kelly's (1955) personal construct theory and its cognitive mapping tool known as the repertory grid (RepGrid). This article expounds on the potential of this technique to IS researchers by considering the variety of ways the RepGrid may be employed. The flexibility of the RepGrid is illustrated by examining published studies in IS. The diagnostic qualities of the RepGrid and its mapping outcomes can be used for practical intervention at the individual and organizational levels.

The repertory grid technique: a method for the study of cognition in information systems

Important issues need raising before detailed knowledge acquisition can take place for designing expert systems. It is futile to try acquiring knowledge and constructing systems unless it is known what it is for.

Anna Hart

Papers

Knowledge acquisition for expert systems