Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

As a system scientist doing modeling and simulation, I have been interested for some time in ways that modeling and simulation and AI could be of value to each other. After all, both areas have their roots in putting knowledge into useful representations. I have speculated (AI Magazine, summer 1989, pp. With respect to breadth of coverage and potential readership, Artificial Intelligence, Simulation, and Modeling does provide a broad survey of current research, but it is written from an AI perspective and will find a greater readership among AI researchers than simulationists. Mark E. Lacy is manager of computational The cover to Expert Systems in Business: A Practical Approach by Michael L. Barrett and Annabel C. Beerel (Ellis Horwood Limited, Chichester, England, 1988, 259 pages, $36.95, ISBN O-7458-0269-9) contains an abstract design in colors of violet, brilliant green, and dark magenta.

The book is edited by Philip Klahr and the late Donald A. Waterman, both of Rand Corporation. The papers are selected from RAND technical reports published from 1977 to 1985. The book is most valuable to people learning knowledge engineering. Four of the papers provide interesting glimpses at the problems involved in transforming knowledge about a domain into computer representations. In addition, the book contains one or two interesting papers for researchers in each of the areas of knowledge acquisition, reasoning with uncertainty, and distributed problem solving.

As a system scientist doing modeling and simulation, I have been interested for some time in ways that modeling and simulation and AI could be of value to each other. After all, both areas have their roots in putting knowledge into useful representations. I have speculated (AI Magazine, summer 1989, pp. With respect to breadth of coverage and potential readership, Artificial Intelligence, Simulation, and Modeling does provide a broad survey of current research, but it is written from an AI perspective and will find a greater readership among AI researchers than simulationists. Mark E. Lacy is manager of computational The cover to Expert Systems in Business: A Practical Approach by Michael L. Barrett and Annabel C. Beerel (Ellis Horwood Limited, Chichester, England, 1988, 259 pages, $36.95, ISBN O-7458-0269-9) contains an abstract design in colors of violet, brilliant green, and dark magenta.

This advertisement might be posted by any manager delegatcd the responsibility for investigating the applications and market possibilities of expert systems for his/her company . To the rescue have come the authors whose books are reviewed in this article. Each author provides answers to some of the questions raised by those considering the use of expert systems on microcomputers: What are expert systems? Can they be implemented on a PC? Have any successful PC applications been created? Do I really need an expert system?

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

Department of Computer Science Carnegie-Mellon University Pittsburgh, Pennsylvania 15221 RI is a rule-based program that configures VAX-I 1 computer systems. Given a customer's purchase order, it determines what, if any, substitutions and additions have to be made to the order to make it consistent and complete and produces a numnber of diagrams showing the spatial and logical relationships among the 90 or so components that typically constitute a system. The program has been used on a regular basis by Digital Equipment Corporation's manufacturing organization since January of 1980. Rl has sufficient knowledge of the configuration domain and of the pecularities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do; thus it requires little search in order to configure a computer system. The approach RI takes to the configuration task and the way its knowledge is represented has been described elsewhere [McDermott 80a, MC Dermott 80b].

This article describes one person's experience in coming from an academic environment to work at Digital Equipment Corpo I've divided this history into two distinct parts. AI and DEC's entry into the AI market, DEC engineers were This article is an edited version of Dr Polit's presentation at the Technology Transfer Symposium held at the AAAI-83 conference Building Expert Systems I'll now give a brief review of the steps involved in building expert systems as they are described by many researchers. The five steps involved in building an expert system are: Step 1: problem recognition, Step 2: task definition, Step 3: initial design, Step 4: knowledge acquisition, and Step 5: system maintenance. Frequently, the problem is perceived as a bottleneck in a larger process; sometimes it is a scarcity of traiued personnel. Second, during step 2, researchers must define the functions the AI system will perform.

The need for online diagnostics in the electric powergeneration industry is driven by a number of significant factors . Due to the low number of new power plants being built by electric utilities, the average age of existing power plant equipment in the United States and its susceptibility to failure is increasing rapidly. Figure 1 shows the percentage of power-generation equipment over 20 years old as a function of year. Note the rapid increase of average age after 1980 and the fact that by the year 2000 fully 50 percent of all generation equipment in the United States will be over 20, the oldest average age of power plant equipment ever experienced by U.S. utilities. Thus, there is a need to know what the actual operating condition of the equipment is at all times, so that outages can be avoided by taking corrective actions at the earliest possible time and by preplanning for outages if they become necessary in order to to minimize their length.

We use our experience with the Dipmeter Advisor system for well-log interpretation as a case study to examine the development of commercial expert systems. We discuss the nature of these systems as we see them in the coming decade, characteristics of the evolution process, development methods, and skills required in the development team. We argue that the tools and ideas of rapid prototyping and successive refinement accelerate the development process. We note that different types of people are required at different stages of expert system development: Those who are primarily knowledgeable in the domain, but who can use the framework to expand the domain knowledge; and those who can actually design and build expert system tools and components We also note that traditional programming skills continue to be required in the development of commercial expert systems Finally, we discuss the problem of technology transfer and compare our experience with some of the traditional wisdom of expert system development. We have observed during this effort that the development of a commercial expert system imposes a substantially different set of constraints and requirements in terms of characteristics and methods of development than those seen in the research environment.