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See also: Evaluating influence diagrams with decision circuits. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)Operations Research, 34, 871-882

In AAAI-86, pp. 284–288.

See also: 2005 AAAI Classic Paper Awards summary of significance by Tom Mitchell in AI Magazine 26(4), 2005.Proc. AAAI-86

Machine Learning, 1, 145–176.

See also:A Fundamental Tradeoff in Knowledge Representation and Reasoning. Slides. Department of Computer and Information Science. Norwegian University of Science and Technology. IT3706 - Knowledge Representation and Modelling, 2005.Knowledge Representation and Reasoning. Morgan Kaufmann, 2004.Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989.Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (1st ed.). James Allen, Ronald J. Brachman, Erik Sandewall, Hector J. Levesque, Ray Reiter, and Richard Fikes (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Annual Review of Computer Science Vol. 1: 255-287

The technology for building knowledge-based systems by inductive inference from examples hasbeen demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directionsMachine Learning, 1, p. 81-106

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It is certainly important that this turns Another purpose of the book might be to serve as out to be the correct pairing of adjectives with nouns.