An introduction to knowledge engineering / Simon Kendal, Malcolm Creen.

Includes bibliographical references (p. 283) and index.

An Introduction to Knowledge Engineering -- Data, Information and Knowledge -- Skills of a Knowledge Engineer -- An Introduction to Knowledge Based Systems -- Types of Knowledge Based System-Expert Systems -- Neural Networks -- Case Based Reasoning -- Genetic Algorithms -- Intelligent Agents -- Data Mining-Knowledge Acquisition -- Knowledge Representation and Reasoning -- Using Knowledge -- Logic, Rules and Representation -- Developing Rule Based Systems -- Semantic Networks -- Frames -- Expert System Shells, Environments and Languages 169 -- Expert System Shells -- Expert System Development Environments -- Use of AI Languages -- Lifecycles and Methodologies -- The Need for Methodologies -- Blackboard Architectures -- Problem Solving Methods -- KADS-HyM (the Hybrid Methodology) -- Building a well Structured Application Using Aion BRE -- Uncertain Reasoning -- Hybrid Knowledge -- Based Systems -- Index.

Knowledge Engineering, as a discipline and a profession, has emerged from the practical application of decades of research into artificial intelligence and intelligent systems. In particular, knowledge engineering refers to the development of systems that use knowledge, rather than data, to solve many novel computing problems. This is achieved by the application of computing techniques, closely associated with human cognitive processes, for transforming data into knowledge. An Introduction to Knowledge Engineering presents a simple but detailed exploration of current and established work in the field. Its simple yet comprehensive treatment of knowledge based systems will provide the reader with a substantial grounding in such technologies as: •expert systems •neural networks •genetic algorithms •case based reasoning systems •data mining •intelligent agents. The text includes activities and self assessments that provide opportunities for the reader to reflect on and reinforce their understanding of the concepts as they progress. The book will be suitable for both undergraduate and postgraduate students in computing science and related disciplines such as Knowledge Engineering, Artificial Intelligence, Intelligent systems, Cognitive Neuroscience, Robotics and Cybernetics. In addition, it is expected that anyone interested or involved in sophisticated information system developments will find the book a valuable source of ideas and guidance.

The authors use a refreshing and novel 'workbook' writing style which gives the book a very practical and easy to use feel. It includes methodologies for the development of hybrid information systems, covers neural networks, case based reasoning and genetic algorithms as well as expert systems. Numerous pointers to web based resources and current research are also included. The content of the book has been successfully used by undergraduates around the world. It is aimed at undergraduates and a strong maths background is not required.

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