The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

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Computational aerodynamics and artificial intelligence

In this survey, we will provide a short survey and classification on the current work in special purpose architectures to support AI applications. In spite of the growing importance of AI applications, work in the area of designing AI architectures are so diversified that articles were published in other areas besides Al, ranging from psychology, medicine, manufacturing, computer architecture, software engineering, and database management to industrial engineering, operations research, and the list grows. The literature search is also complicated by the fact that with the development of the Fifth-Generation Computer Systems, some work in this area is very recent and was published in many foreign countries. During our literature search to compile this survey, we systematically went through over sixty different journals published in various countries and proceedings from over fifty conferences in the last twenty years and over seventy books.

A survey on special purpose computer architecture for AI

In this survey, we will provide a short survey and classification on the current work in special purpose architectures to support AI applications In spite of the growing importance of AI applications, work in the area of designing AI architectures are so diversified that articles were published in other areas besides Al, ranging from psychology, medicine, manufacturing, computer architecture, software engineering, and database management to industrial engineering, operations research, and the list grows The literature search is also complicated by the fact that with the development of the Fifth-Generation Computer Systems, some work in this area is very recent and was published in many foreign countries During our literature search to compile this survey, we systematically went through over sixty different journals published in various countries and proceedings from over fifty conferences in the last twenty years and over seventy books

Survey on special purpose computer architectures for AI

It is observed that advances in information technology are drastically changing the profession of technical communication and the role of the technical communicator in developing and managing information. It is argued that educators must face the challenge posed by these advances to prepare students for the profession as it is now and as it will be in the future. It is suggested that one answer to this challenge is experiential learning theory, which places experience at the center of the learning process. >

Preparing the technical communicator of the future

Development of Systems Science — Past, Present, and Future

Major areas of artificial intelligence (AI) research as it impacts on next-generation computing are explored in five short, related articles. The first article provides an overview of the background and current goals of AI. The remaining four discuss, in turn: The codification of human knowledge for machine reading; helping computers understand natural languages; continuous speech recognition; and computer vision with imperfect inputs.

The building blocks: Artificial intelligence, software engineering, computer architecture, and very large-scale integration will play major roles: Artificial intelligence

V. Zue

Papers

The building blocks: Artificial intelligence, software engineering, computer architecture, and very large-scale integration will play major roles: Artificial intelligence