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AAAI News

AI Magazine

Topics An all-star panel assembled to pose life, and a wealth of other topics were of the technical papers spanned a "Challenge Problems for Artificial Intelligence."


From Data Mining to Knowledge Discovery in Databases

AI Magazine

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.


Applying Case-Based Reasoning to Manufacturing

AI Magazine

's central purpose is to find the most The use of composite materials, Composite part fabrication requires two especially in aerospace applications, is on the major steps: layup and curing. Layup is the increase because of their unique weight and painstaking process in which multiple layers strength qualities. Depending on the orientation of graphite and fiberglass composite material of the graphite fibers, a part can be are fitted by hand on the exterior of a contoured extremely flexible in one direction but rigid mold. In addition, a part made from takes from two to seven days, depending on composite material is both lighter and the size of the mold and the skill of the technician. The increased use of In the second step, curing, the molded graphite parts, as well as the high cost of a composite material is hardened by pressurized spoiled part (as much as $50,000 for a single heating in a large convection autoclave.


Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network

Neural Information Processing Systems

Classification is widely used in the animal kingdom. Identifying an item as food is classification. Assigning words to objects, actions, feelings, and situations is classification. The purpose of this work is to introduce a new neural network, the Binary Diamond, which can be used as a general purpose classification tool. The design and operational mode of the Binary Diamond are influenced by observations of the underlying mechanisms that take place in human classification processes.


Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network

Neural Information Processing Systems

Classification is widely used in the animal kingdom. Identifying an item as food is classification. Assigning words to objects, actions, feelings, and situations is classification. The purpose of this work is to introduce a new neural network, the Binary Diamond, which can be used as a general purpose classification tool. The design and operational mode of the Binary Diamond are influenced by observations of the underlying mechanisms that take place in human classification processes.


Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network

Neural Information Processing Systems

Classification is widely used in the animal kingdom. Identifying an item as food is classification. Assigning words to objects, actions, feelings, and situations is classification. The purpose of this work is to introduce a new neural network, the Binary Diamond, which can be used as a general purpose classification tool. The design and operational mode of the Binary Diamond are influenced by observations of the underlying mechanisms that take place in human classification processes.


Knowledge-Based Systems Research and Applications in Japan, 1992

AI Magazine

This article summarizes the findings of a 1992 study of knowledge-based systems research and applications in Japan. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.


Pitch Expert: A Problem -- Solving System for Kraft Mills

AI Magazine

PITCH EXPERT was developed to make expertise available to mill-site engineers to solve pitch problems in kraft pulp mills. These problems have been estimated to cause losses to the Canadian pulp and paper industry in excess of $80 million each year. The design of the system took into account not only the complexity of the process interactions and the need for accuracy and completeness of recommendations but also the ongoing need for training mill personnel and the requirement that the system be maintainable and expandable without the constant involvement of the developers. PITCH EXPERT is now accessible by modem, and the savings achieved through use of the system covered the development costs within six months of release.


Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency

Neural Information Processing Systems

In a Bayesian framework, we give a principled account of how domainspecific prior knowledge such as imperfect analytic domain theories can be optimally incorporated into networks of locally-tuned units: by choosing a specific architecture and by applying a specific training regimen. Our method proved successful in overcoming the data deficiency problem in a large-scale application to devise a neural control for a hot line rolling mill. It achieves in this application significantly higher accuracy than optimally-tuned standard algorithms such as sigmoidal backpropagation, and outperforms the state-of-the-art solution.


Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency

Neural Information Processing Systems

In a Bayesian framework, we give a principled account of how domainspecific prior knowledge such as imperfect analytic domain theories can be optimally incorporated into networks of locally-tuned units: by choosing a specific architecture and by applying a specific training regimen. Our method proved successful in overcoming the data deficiency problem in a large-scale application to devise a neural control for a hot line rolling mill. It achieves in this application significantly higher accuracy than optimally-tuned standard algorithms such as sigmoidal backpropagation, and outperforms the state-of-the-art solution.