An Introduction to Knowledge Engineering by S. L. Kendal, M. Creen (auth.)

By S. L. Kendal, M. Creen (auth.)

Knowledge Engineering, as a self-discipline and a occupation, has emerged from the sensible software of a long time of analysis into man made intelligence and clever structures. specifically, wisdom engineering refers back to the improvement of platforms that use wisdom, instead of facts, to unravel many novel computing difficulties. this can be completed via the appliance of computing recommendations, heavily linked to human cognitive procedures, for reworking info into knowledge.

An advent to wisdom Engineering provides an easy yet exact exploration of present and demonstrated paintings within the box. Its basic but complete remedy of information dependent platforms will give you the reader with a considerable grounding in such applied sciences as:

•expert systems

•neural networks

•genetic algorithms

•case established reasoning structures

•data mining

•intelligent agents.

The textual content contains actions and self tests that supply possibilities for the reader to mirror on and toughen their realizing of the thoughts as they progress.

The booklet could be compatible for either undergraduate and postgraduate scholars in computing technology and similar disciplines similar to wisdom Engineering, man made Intelligence, clever structures, Cognitive Neuroscience, Robotics and Cybernetics.

In addition, it's anticipated that any one or excited by refined details procedure advancements will locate the ebook a helpful resource of rules and advice.

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Additional resources for An Introduction to Knowledge Engineering

Example text

Prentice-Hall: Englewood Cliffs, NJ. Drucker, P. F. (1988). The coming of the new organisation. Harvard Business Review, 66(1):39–48. Fensel, D. (1995). The Knowledge Acquisition and Representation Language KARL. Kluwer Academic Publishers: Amsterdam. Harry, M. (1994). Information Systems in Business. Pitman Publishing: Boston, MA, p. 50. Hayes, R. (1992). The measurement of information. In Vakkari, P. and Cronin, B. (editors), Conceptions of Library and Information Science. Taylor Graham: London, pp.

R Retrieve past cases matching these features most closely. r Select the cases most relevant to the current problem. r Where necessary, adapt the stored cases to solve the current problem. r Validate the new solution and store as a new case. r Where a case match is not found, then find an alternative solution and record both problem and solution. Where more that one case match is found, the CBR system will need to: r resolve any ambiguities if multiple solutions are found r recognise that multiple solutions may sometimes be acceptable.

On the basis of what you have learned so far about expert systems, suggest three main limitations and three main strengths. Feedback 4 You should have been able to suggest three of the following limitations: r Narrow knowledge domain, they are developed to solve a very specific problem r Knowledge acquisition from experts r Need for commitment from expert(s) r Cannot generalise r Cannot apply ‘common sense’ r Cost of development and maintenance r Expert systems think mechanically and lack the power of human creativity r Expert systems require regular maintenance to update with new knowledge.

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