The rapid advances in information technology are causing a fundamental change in the way we do our business. Within our manufacturing business today, various parts of the organization are " reasoning " about "engineered products." The everyday problem-solving activity within the organization can be thought of as conducted by a network of experts knowledgeable about the products and the physical and paperwork processes that constitute the business, that is, the knowledge network. The focus of our attention has not been just at the factory level; we have been addressing the order-process cycle: marketing, sales, order administration, manufacturing, distribution, and field service. This cycle can be thought of as outer loop of the knowledge network. Also, we recently began addressing the inner loop. This loop is the product life cycle : marketing and new product requirements, design and manufacturing startup, and volume or steady-state manufacturing. This article describes DEC's internal strategy for applying artificial intelligence (AI) to manufacturing processes and problems above the work-cell level. In addition to an overview of this knowledge network, we feature DEC's newest system in order processing : the configuration-dependent sourcing (CDS) expert. Project experience on this system, which deals with the assignment of fulfillment sites (factories) to line items in computer system orders, is also described.

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.

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.

I review the research leading from the GUIDON rule-based tutoring system, including the reconfiguration of MYCIN into NEOMYCIN and NEOMYCIN's generalization in the heuristic classification shell, HERACLES. The presentation is organized chronologically around pictures and dialogues that represent conceptual turning points and crystallize the basic ideas. My purpose is to collect the important results in one place, so they can be easily grasped. In the conclusion, I make some observations about our research methodology.

This is the second part of a two-part article describing AI work at the NASA Johnson Space Center (JSC). Research and applications work in AI is being conducted by several groups at JSC. These are primarily independent groups that interact with each other on an informal basis. In the Research and Engineering Directorate, these groups include (1) the Artificial Intelligence and Information Sciences Office, (2) the Simulation and Avionics Integration Division, (3) the Avionics Systems Division, and (4) the Tracking and Communications Division. In the Space Operations Directorate, these groups include (1) the Mission Planning and Analysis Division (MPAD) - Technology Development and Applications Branch, (2) the Spacecraft Software Division, and (3) the Systems Division - Systems Support Section. This second part of the article describes the AI work in the Space Operations Directorate. The first part of the article, published in the last week of AI Magazine, (7:1, Summer 1986) described the AI work in the Research and Engineering Directorate.

I review the research leading from the GUIDON rule-based tutoring system, including the reconfiguration of MYCIN into NEOMYCIN and NEOMYCIN's generalization in the heuristic classification shell, HERACLES. The presentation is organized chronologically around pictures and dialogues that represent conceptual turning points and crystallize the basic ideas. My purpose is to collect the important results in one place, so they can be easily grasped. In the conclusion, I make some observations about our research methodology.

The interpretation of images of postal mail pieces is The Vision Group the domain of this investigation. Our efforts have included It is becoming increasingly important for vision researchers the development of various operators for visual data processing in diverse fields to interact, and the Vision Group at SUNY and image segmentation. The invocation of these Buffalo was formed to facilitate that interaction Current routines and the interpretation of the information they return membership includes 25 faculty and 25 students from 10 is determined by a control structure that uses a variant departments (computer science, electrical and computer of relaxation combined with a rule-based methodology.

THE AI MAGAZINE Summer, 1986 55 building one of the first stored-program digital computers, AI also had its share of controversy, however, at Rand the JOHNNIAC (see Figure 1) (Gruenberger, 1968);l and elsewhere. Given its quick rise to popularity and its George Dantzig and his associates were inventing linear ambitious predictions (Simon & Newell, 1958), AI soon programming (Dantzig, 1963); Les Ford and Ray Fulkerson had its critics, and one of the most prominent, Hubert were developing techniques for network flow analysis Dreyfus, published his famous critique of AI (Dreyfus, (Ford & Fulkerson, 1962); Richard Bellman was developing 1965) while he was consulting at Rand. In addition, the his ideas on dynamic programming (Bellman, 1953); early promise of automatic machine translation of text Herman Kahn was advancing techniques for Monte Carlo from one language to another (the emphasis at Rand was simulation (Kahn, 1955); Lloyd Shapley was revolutionizing on translation from Russian to English) produced only game theory (Shapley, 1951-1960); Stephen Kleene was modest systems, and the goal of fully automated machine advancing our understanding of finite automata (Kleene, translation was abandoned in the early 1960s.

General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.