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3.4 Artificial Intelligence Methodologies

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Borgelt, Christian, and Rudolf Kruse. 2006. Section 3.4 Artificial Intelligence Methodologies, pp. 153-168 of Chapter 3 Methods, Algorithms, and Software, in CIGR Handbook of Agricultural Engineering Volume VI Information Technology. Edited by CIGR--The International Commission of Agricultural Engineering; Volume Editor, Axel Munack. St. Joseph, Michigan, USA: ASABE.  .(doi:10.13031/2013.21671)
Authors:   C. Borgelt and R. Kruse
Keywords:   Artificial intelligence, Knowledge representation, Reasoning, Planning, Learning

Artificial Intelligence is concerned with imitating the intelligent behavior of natural systems, such as animals and human beings, with artifacts such as computers and robots. It involves understanding how knowledge -especially uncertain and vague knowledge -can be represented so that it can be stored in computer memory and inferences can be drawn from it automatically, how decisions can be made and plans of action can be devised based on the stored knowledge, and how computerprocessable knowledge can be acquired by interrogating human experts or by learning from example data.

Clearly, this section can only highlight some core methodologies and touch upon a few basic approaches. An interested reader is referred to the more-detailed expositions that can be found, for instance, in [1,2]. Textbooks for specific areas of artificial intelligence (formal logic, fuzzy systems, artificial neural networks, etc.) are pointed out in the corresponding sections.

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