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authors Reich, Yoram
year 1988
title Machine Learning for Expert Systems : Motivation and Techniques
source i-iii, 51 p. : some ill Pittsburgh, PA: Engineering Design Research Center, CMU, June, 1988. EDRC 12-27-88. includes bibliography. First generation expert systems suffer from two major problems: they are brittle and their development is a long, effortful process. Few successful expert systems for real world problems have been demonstrated. In this paper, learning, the key to intelligent behavior and expertise, is described as the answer to both expert systems deficiencies. Machine learning techniques are described, with their applicability to expert systems. A framework to organize machine learning techniques is provided. The description is followed by examples taken from the structural design domain. AI / learning / expert systems / structures / techniques. 37. Requicha, Aristides A. G. 'Mathematical Models of Rigid Solid Objects -- Production Automation Project.' Rochester, NY: College of Engineering & Applied Science, University of Rochester, November, 1977. [3], 37 p. : ill.
summary Computational models of solid objects are potentially useful in a variety of scientific and engineering fields, and in particular in the field of design and manufacturing automation for the mechanical industries. In recent years a multitude of modelling systems have been implemented both by research laboratories and commercial vendors, but little attention has been paid to the fundamental theoretical issues in geometric modelling. This has led to severe difficulties in assessing current and proposed systems, and in distinguishing essential capabilities and limitations from user conveniences and efficiency considerations. This paper seeks a sharp mathematical characterization of 'rigid solids' in a manner that is suitable for studies in design and production automation. It draws heavily on established results in modern geometry and topology. Relevant results scattered throughout the mathematical literature are placed in a coherent framework and presented in a form accessible to engineers and computer scientists. A companion paper is devoted to a discussion of representational issues in the context set forth by this paper
keywords solid modeling, geometric modeling
series CADline
references Content-type: text/plain
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