Home Artificial Intelligence • Download e-book for iPad: An Introduction to Knowledge Engineering by Simon Kendal, Malcolm Creen

Download e-book for iPad: An Introduction to Knowledge Engineering by Simon Kendal, Malcolm Creen

By Simon Kendal, Malcolm Creen

ISBN-10: 1846284759

ISBN-13: 9781846284755

The authors use a fresh and novel 'workbook' writing variety which supplies the booklet a really useful and simple to exploit believe. It comprises methodologies for the advance of hybrid details platforms, covers neural networks, case dependent reasoning and genetic algorithms in addition to specialist structures. a number of tips to internet established assets and present examine also are integrated. The content material of the ebook has been effectively utilized by undergraduates world wide. it truly is aimed toward undergraduates and a powerful maths history isn't really required.

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Extra resources for An Introduction to Knowledge Engineering

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Human knowledge may be lost. That is humans tend to die eventually, or their knowledge may be lost in other ways such as brain disease or simply changing jobs. Human knowledge can only be accessed in one place at one time—that is where the expert happens to be. However, an expert system can be duplicated as many times as required or accessed online. In contrast expert systems tend to lack: r common sense—humans may draw conclusions based on their overall view of the world; expert systems do not have this information r inspiration or intuition—computers tend to lack these attributes r flexibility to apply their knowledge outside a relevant domain.

3. The error, for each of the nodes, is then attributed to each of the nodes in the previous layer (on the basis of the strength of the connection). Thus the error is passed back through the network. 4. , the nodes in the preceding layer are adjusted, until the errors are propagated backwards through the entire network, finally reaching the input layer (hence the term back propagation). One minor complication remains—this training algorithm does not work when the output from a node can only be 0 or 1.

6. Expert systems Neural networks (NNs) Case-based reasoning (CBR) Genetic algorithms Intelligent agents Data mining. Objectives By the end of the chapter you will be able to: r describe the characteristics of a knowledge-based system r explain the main elements of knowledge-based systems and how they work r evaluate the advantages and limitations of knowledge-based systems r identify appropriate contexts for the use of particular types of knowledge-based systems. 26 Types of Knowledge-Based Systems 27 SECTION 1: EXPERT SYSTEMS Introduction This section provides you with an introduction to expert systems and their use within knowledge engineering.

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An Introduction to Knowledge Engineering by Simon Kendal, Malcolm Creen

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