Artificial Intelligence

An Introduction to Knowledge Engineering by Simon Kendal, Malcolm Creen

By Simon Kendal, Malcolm Creen

An advent to wisdom Engineering provides an easy yet exact exp- ration of present and validated paintings within the ?eld of knowledge-based structures and comparable applied sciences. Its therapy of the expanding number of such structures is designed to supply the reader with a considerable grounding in such techno- gies as professional structures, neural networks, genetic algorithms, case-based reasoning structures, facts mining, clever brokers and the linked thoughts and meth- ologies. the fabric is strengthened by way of the inclusion of diverse actions that supply possibilities for the reader to have interaction of their personal study and re?ection as they development throughout the e-book. moreover, self-assessment questions enable the coed to ascertain their very own knowing of the ideas coated. The ebook could be compatible for either undergraduate and postgraduate scholars in computing technology and similar disciplines equivalent to wisdom engineering, arti?cial intelligence, clever platforms, cognitive neuroscience, robotics and cybernetics. vii Contents Foreword vii 1 An advent to wisdom Engineering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 part 1: information, info and information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 part 2: talents of a data Engineer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 part three: An creation to Knowledge-Based structures. . . . . . . . . . . . . . . . . 18 2 varieties of Knowledge-Based structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 part 1: specialist platforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 part 2: Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 part three: Case-Based Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . fifty five part four: Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . sixty six part five: clever brokers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . seventy four part 6: information Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . eighty three three wisdom Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 four wisdom illustration and Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 part 1: utilizing wisdom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 part 2: common sense, principles and illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 part three: constructing Rule-Based platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 part four: Semantic Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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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.

Knowledge Systems Design. 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.

From there you select a sample vector randomly and search the map of weight vectors to find which weight best represents that sample. Since each weight vector has a location, it also has neighbouring weights that are close to it. The weight that is chosen is rewarded by being able to become more like that randomly selected sample vector. In addition to this reward, the neighbours of that weight are also rewarded by being able to become more like the chosen sample vector. From this step we increase t a small amount because the number of neighbours and how much each weight can learn decreases over time.

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