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Fuzzy Systems and Machine Intelligence Abstract: Our natural language is perhaps the most powerful form of communicating information for any given problem or situation. Combining natural language and numerical information into fuzzy systems provides the framework to represent knowledge, constraints and inference procedures. Fuzzy systems provide advantages in the development of systems solutions that perform tasks such as automatic modeling, prediction, pattern recognition, and optimal decision making, control and planning. With this, fuzzy systems are an essential tool for industrial and manufacturing systems engineering. Fuzzy logic is a different approach to representing uncertainty - it emphasizes the double meanings of words in describing events - rather than the uncertainty about whether an event will occur, and allows decision-making under that uncertainty.
Also in Carbonell, J., editor, Machine Learning: Paradigms and Methods, MIT Press, Cambridge, MA, 1990. Project information is on-line at http://www.cs.indiana.edu/~leake/projects/swale Turing1950 Turing, A. 1950. Computing machinery and intelligence. Mind 59.
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Telecherics involved human controlling a machine/robot o... ... middle of paper ... ...ially because in this day and age, optimizing tasks and making production quick and efficient is prevalent. As production companies and other large manufacturing firms become more complex and automated, industrial robotics and automation will become an integral part of the industrial world by making facilities in the labor force as cost effective and productive as possible. Works Cited Page Dorf, Richard C. “Applications and Automation.” International Encyclopedia of Robotics 28.3 (1989): 419. Web. Groover, Mikell P., Mitchell Weiss, Roger N. Nagel, and Nicholas G. Odrey.
Haugeland, John, ed. Mind Design II: Philosophy, Psychology, Artificial Intelligence USA: MIT Press, 2000. Hodges, Andrew. Alan Turing and the Turing Test Mar. 15 2005 < http://www.turing.org.uk/turing/scrapbook/test.html> Millar P. H. “On the point of the Imitation Game.” Mind, New Series, Vol.
Morgan Kaufmann Publishers, Inc. Nii, H. Penny. Expert Systems Building Tools: Definitions. 1993. http://itri.loyola.edu/kb/c3_s2.htm. Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems. 1990.
Agile leaders must ensure that knowledge gap is minimal within the organization. By eliminating the knowledge gap, an organization can operate more efficiently and help cut or avoid costs that does not add value to the business. In order to set effective and meaningful IT strategy, leaders should consider designing and architecting an IT organization where IT organizational function, IT architecture and agile development framework are converging well. Another component plays a significant role here which is outsourcing. Agile organization’s decentralized functions along with deeper understanding of applications, software and hardware architecture is advantageous before allocating budget for outsourcing for the agile software development.
Dean, Thomas., Doyle, Jon. (1996). “Strategic Direction in Artificial Intelligence.” ACM Computing Surveys (CSUR), 28(4), 653-670. Klassner, Frank. (1996).