New PDF release: An Introduction to Knowledge Engineering

By Simon Kendal, Malcolm Creen

ISBN-10: 1846284759

ISBN-13: 9781846284755

The authors use a clean and novel 'workbook' writing kind which provides the publication a really sensible and straightforward to take advantage of believe. It comprises methodologies for the improvement of hybrid details structures, covers neural networks, case established reasoning and genetic algorithms in addition to professional structures. a variety of tips to internet dependent assets and present learn also are incorporated. The content material of the e-book has been effectively utilized by undergraduates all over the world. it's aimed toward undergraduates and a powerful maths history isn't really required.

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Extra resources for An Introduction to Knowledge Engineering

Example text

In other words human experts are available who can provide the appropriate knowledge, without ambiguity, to build the expert system rule base. r The knowledge required is scarce—at least in terms of human experts available to provide answers to queries within that knowledge domain. r The problem is recurrent—so the expert system will be used over many geographical locations or a long period of time. r The problem is at the right level of difficulty. In some situations, it may be easier to train more human experts where a limited amount of knowledge is required.

Adaptive Resonance Theory Unsupervised learning is often used to classify data. In this case, the classification is done with a clustering algorithm. Adaptive resonance theory was developed to account for changes in the input data that supervised NNs were not able to handle. Basically, programmers wanted to design a system that could modify itself in response to a changing input environment. If changes are frequent, the ability to adapt is essential for the program. Without this ability to adapt, the system’s accuracy begins to decrease rapidly.

Acquisition module—obtains appropriate knowledge from the human expert and the database ready for input to the KB of the expert system. Knowledge base—retains the knowledge and rules used by the expert system in making decisions. Inference engine—system that reasons to provide answers to problems placed into the expert system. The inference engine uses knowledge from the KB to arrive at a decision. Explanatory interface—to provide the user with an explanation on how the expert system reached its conclusion.

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An Introduction to Knowledge Engineering by Simon Kendal, Malcolm Creen


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