By S. L. Kendal, M. Creen (auth.)
Knowledge Engineering, as a self-discipline and a occupation, has emerged from the sensible program of a long time of analysis into man made intelligence and clever structures. specifically, wisdom engineering refers back to the improvement of structures that use wisdom, instead of information, to unravel many novel computing difficulties. this can be completed by way of the appliance of computing concepts, heavily linked to human cognitive strategies, for remodeling facts into knowledge.
An creation to wisdom Engineering offers an easy yet targeted exploration of present and demonstrated paintings within the box. Its easy but entire remedy of data dependent structures will give you the reader with a considerable grounding in such applied sciences as:
•case established reasoning platforms
The textual content contains actions and self exams that supply possibilities for the reader to mirror on and make stronger their knowing of the ideas as they progress.
The e-book could be compatible for either undergraduate and postgraduate scholars in computing technology and comparable disciplines comparable to wisdom Engineering, man made Intelligence, clever structures, Cognitive Neuroscience, Robotics and Cybernetics.
In addition, it truly is anticipated that anybody or keen on subtle info process advancements will locate the ebook a important resource of rules and counsel.
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Additional resources for An Introduction to Knowledge Engineering
197. Senn, J. A. (1990). Information Systems in Management. Wadsworth Publishing: Belmont, CA, p. 58. Zachman, J. (1987). A framework for information systems architecture. IBM Systems Journal, 26(3):276–292. 2 Types of Knowledge-Based Systems Introduction This chapter builds on the brief introduction to different types of knowledge-based systems from the ﬁrst chapter and provides you with the opportunity to explore them in greater depth. The chapter consists of six sections: 1. 2. 3. 4. 5. 6. Expert systems Neural networks (NNs) Case-based reasoning (CBR) Genetic algorithms Intelligent agents Data mining.
When a match occurs you will recognise the face in the photograph. By storing only the essential features your brain has reduced the complexity of the data it needs to store. Why is This Unsupervised Learning? For many of the things you learn there is a right and wrong answer. Thus if you were to make a mistake a teacher could provide a correct response. This is supervised learning. SOMs group similar items of data together. When picking out the features of a face various features can be chosen, eye colour, the shape of the nose etc.
R Reuse knowledge from those cases which help in solving the current problem. r Revise the proposed solution, using the case information. r Retain details of the current (now hopefully solved) problem as a new case for future reference. Cases do not need to be understood by the knowledge engineer in order to be stored. 58 An Introduction to Knowledge Engineering Activity 15 Bearing in mind the steps involved in CBR, draw a diagram to show the CBR process. Feedback 24 One possible solution is shown in the diagram below.