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.

The Parametric Interpretation Expert System (PIES) is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes. The system transforms hundreds of measurements into a concise statement of all the overall health of the process and the nature and probable cause of any anomalies. A key feature of PIES is the structure of the knowledge base, which reflects the way fabrication engineers reason causally about semiconductor failures. This structure permits fabrication engineers to do their own knowledge engineering, to build the knowledge base, and then to maintain it to reflect process modifications and operating experience. The approach appears applicable to other process control and diagnosis tasks.

A new computer language for manufacturing is being used to link complex systems of equipment whose components are supplied by multiple vendors. The Cell Management Language (CML) combines computational tools from rule-based data systems, object-oriented languages, and new tools that facilitate language processing. These language tools, combined with rule processing, make it convenient to build new interpreters for interfacing and understanding a range of computer and natural languages ; hence, CML is being used primarily to define other languages in an interpretive environment, that is, as a meta-interpreter. For example, in CML it is quite easy to build an interpreter for machine tool languages that can understand and generate new part programs. Once interpreters for different machine and human languages have been constructed, they can be linked together into a system of interpreters. These interpreters can be used to make intelligent decisions for systemwide action planning and diagnostic error recovery. CML is being used in the factory environment to make turbine blade performs and has proven to greatly simplify the task of building complex control systems.

To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.

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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.

The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described. The artificial intelligence (AI) diagnostic program has been diagnosing seven large utility generators since July 1984 and has correctly diagnosed a significant number of generator and instrumentation problems. Issues such as a centralized approach, rule base quality control, and the range of resources needed for a successful product are discussed.

The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.

This article provides some basic assistance to entrepreneurs involved in artificial intelligence, offering a synthesis of standard business-planning and capital-raising practices. Three main areas are discussed: (1) developing a corporate strategy, (2) developing a business plan that works, and (3) approaching sources of capital.

In this article, we present a programming language for expressing classificatory problem solvers. CSRL (Conceptual Structures Representation Language) provides structures for representing classification trees, for navigating within those trees, and for encoding uncertainly judgments about the presence of hypotheses. We discuss the motivations, theory, and assumptions that underlie CRSL. Also, some expert systems constructed with CSRL are briefly described.