The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

This report contains the reading list for the Qualifying Examination in Artificial Intelligence. Areas covered include search, representation, reasoning, planning and problem solving, learning, expert systems, vision, robotics, natural language, perspectives and AI programming. An extensive bibliography is also provided.

Letters to the Editor

Other interesting topics in superb, it still contains inadequacies. This statement is more this chapter include nonmonotonic reasoning and modal and a testament to how remarkably difficult it is to write an adequate intentional logic. Perhaps the most intriguing chapter is introductory AI text than it is a criticism of the job done "Memory Organization and Deduction," which touches by the authors. Because there really are no single volumes upon the topics of frame-based representation and deductive yet that provide a satisfactory introduction to AI, the best retrieval and introduces the time-order representation approaches way to approach the problem of selecting text material for an of temporal system analysis and time map management introductory AI course seems to be use a book such as this .

Other interesting topics in superb, it still contains inadequacies. This statement is more this chapter include nonmonotonic reasoning and modal and a testament to how remarkably difficult it is to write an adequate intentional logic. Perhaps the most intriguing chapter is introductory AI text than it is a criticism of the job done "Memory Organization and Deduction," which touches by the authors. Because there really are no single volumes upon the topics of frame-based representation and deductive yet that provide a satisfactory introduction to AI, the best retrieval and introduces the time-order representation approaches way to approach the problem of selecting text material for an of temporal system analysis and time map management introductory AI course seems to be use a book such as this .

Initial Development Approach In the following eight subsections, we present a brief discussion of methodology for expert system development, selection of problem and tools, knowledge engineering and prototype implementation, operational feasibility, and the actual development of a working prototype of a process planning expert system. Methodology for Expert System Development Expert systems require a software development methodology that differs in some respects from those methodologies used for conventional systems. Most knowledge-based development methodologies used by organizations experienced in building expert systems are similar in that they concentrate on the early (feasibility) stages of a project. Very little has been published on the later stages, which are concerned with expert system delivery, integration, and maintenance. During the development of OPGEN, we incorporated the lessons learned in these early stages and revised our original approach to provide for integration and maintenance. Most expert system development methodologies are a variation on the following theme, which paraphrases Haycs-Roth (1985): (1) expert system technology is determined to be relevant to a product; (2) management provides an opportunity for action; (3) a preliminary business application is assembled; (4) a knowledge engineering consultant verifies the opportunity; (5) a knowledge engineering project team is formed and assesses the knowledge; (6) the knowledge engineering project manager plans the project; (7) the user organization Figure 2 OPGEN bzput Circuit Layout Diagram.

Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers. In the second phase, the understanding of point solutions was used to capture the underlying models of project management in distributed project negotiations and comparative analysis. This article provides an overview of the problems, experiments, and the resulting models of project knowledge and constraint-directed negotiation.

Letters to the Editor

This article provides a discussion of factory design and an artificial intelligence (AI) approach to this problem. Major issues covered include knowledge acquisition and representation, design methodology, system architecture, and communication. The facilities design expert systems (FADES developed by the author is presented and described to illustrate issues in factory design.

EDITORIALS This Fall issue marks the first time we have devoted the AI Figure 1 summarizes the results of a survey I ran in 1985. The idea originated a couple of depicts the number of AI based systems in the various stages years ago, and I'm pleased to see the actual implementation. of research, development, field service and production use. Mark Fox, Special Editor for this issue, is to be congratulated It is my guess that the survey represents about 15% of the for a fine job of selecting some of the best authorities in systems currently under development. The incursion of AI the field and working with them to produce an excellent survey into the manufacturing world has reached the point that discussions of the current state of the art in AI for manufacturing. The quality of all the articles was so high that we didn't want to exclude any of them.