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Introduction to the COMTEX Microfiche Edition of Reports on Artificial Intelligence from Carnegie-Mellon University
Abstract
Originally it was Complex Information Processing. That was the name Herb Simon and I chose in 1956 to describe the area in which we are working. It didn't take long before it became Artificial Intelligence (AI). Coined by John McCarthy, that term has stuck firmly, despite continual grumblings that any other name would be twice as fair (though no grumblings by me; I like the present name). Complex Information processing lives on now only in the title of the CIP Working Papers, a series started by Herb Simon in 1956 and still accumulating entries (to 447). However, from about 1965 much of the work on artificial intelligence that was not related to psychology began to appear in technical reports of the Computer Science Department. These reports, never part of a coherent numbered series until 1978, proliferated in all directions. Starting in the early 1970s (on one can recall exactly when), they did become the subject of a general mailing and thus began to form what everyone thinks of as the CMU Computer Science Technical Reports.
... Not only with mIT, Stanford, and Cmu, which are now seen as the main DArPA-supported university computer-science research environments, but with other universities as well ... DArPA began to build excellence in information processing in whatever fashion we thought best.... The DArPA effort, or anything similar, had not been in our wildest imaginings.... (Newell 1984) As AI research progressed, systems tackled increasingly complex and broad-based task domains, thus creating the need for representing domain knowledge more thoroughly and accurately to better support sophisticated computations using the knowledge. The first significant efforts to integrate domain knowledge into AI systems were in human language understanding, mobile robotics (AI Magazine DArPA leadership in AI, 2020), and expert systems. ...
A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from the Defense Advanced Research Projects Agency and other agencies. This article highlights several decades of advances in knowledge representation and reasoning methods, paying particular attention to research on planning and on the impact of the Defense Advanced Research Projects Agency’s support.
In this paper, we report the results of the first phase of a three-part study of the emerging role of Artificial Intelligence (AI) as an academic discipline. The stratification among journals that are formal communication vehicles for AI research is described and analyzed. Based on a survey of business faculty from AACSB-accredited schools around the country, 30 leading AI journals are ranked and evaluated, in terms of their academic quality and reputation. The paper also reports on the progress toward the development of a cumulative tradition for AI and its foundation. The second phase of this research focuses on the knowledge utilization of AI researchers and practitioners. In the third phase, the findings from the above two studies will be analyzed and used as a basis to develop a taxonomy of research for Artificial Intelligence.
technology. This paper reviews critical success factors associated with expert system implementation. Previous research is examined and categorized with respect to organizational, technological and behavioral considerations. Both positive and negative factors associated with the diffusion of expert system technology are examined. Particular attention is given to the identification of neglected expert system implementation issues along with suggestions to address these barriers to expert system diffusion.
The JOHNNIAC Open-Shop System (JOSS) is an experimental, on-line, time-shared computing system which has been in daily use by staff members of The RAND Corporation since January 1964. It was designed to give the individual scientist or engineer an easy, direct way of solving his small numerical problems without a large investment in learning to use an operating system, a compiler, and debugging tools, or in explaining his problems to a professional computer programmer and in checking the latter's results. The ease and directness of JOSS is attributable to an interpretive routine in the JOHNNIAC computer which responds quickly to instructions expressed in a simple language and transmitted over telephone lines from convenient remote electric-typewriter consoles. An evaluation of the system has shown that in spite of severe constraints on speed and size of program, and the use of an aging machine of the vacuumtube era, JOSS provides a valuable service for computational needs which cannot be satisfied by conventional, closed-shop practice.
The linear programing algorithms available for optimizing the routing of shipments in multi-plant, multi-destination systems cannot, in the current state of knowledge, be applied directly to the more general problem of determining the number and location of regional warehouses in large-scale distribution networks.
This paper outlines a heuristic computer program for locating warehouses and compares it with recently published efforts at solving the problem either by means of simulation or as a variant of linear programing. The heuristic approach outlined in this paper appears to offer significant advantages in the solution of this class of problems in that it (1) provides considerable flexibility in the specification (modeling) of the problem to be solved, (2) can be used to study large-scale problems, that is, complexes with several hundred potential warehouse sites and several thousand shipment destinations, and (3) is economical of computer time. The results obtained in applying the program to small scale problems have been equal to or better than those provided by the alternative methods considered.
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