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.

Teknowledge, Inc. Palo Alto, CA 9&'01 Over the past few years, the character of the AI community has changed. Artificial intelligence is not in itself a commercial field, but a science and a technology As an academic discipline, it is a collection of concepts and ideas which are appropriate for rcscarch, but which cannot be marketed. However, artificial intelligence is the scientific foundation for several growing commercial technologies The three most important are robotics and vision (which for simplicity's sake I will consider as one field), natural language processing, and knowledge engineering. Supporting markets are forming which will provide hardware and software tools for use in these three areas. The potential value of Artificial Intelligence can be bctter understood by contrasting it with natural intelligence.

Real-time domains present a new and challenging environment for the application of knowledge-based problem-solving techniques. However, a substantial amount of research is still needed to solve many difficult problems before realtime expert systems can enhance current monitoring and control systems. In this article, we examine how the real-time problem domain is significantly different from those domains which have traditionally been solved by expert systems. We conduct a survey on the current state of the art in applying knowledge-based systems to real-time problems and describe the key issues that are pertinent in a real-time domain. The survey is divided into three areas: applications, tools, and theoretic issues.

The author acknowledges the kind cooperation of Professor Aravind Joshi, IJCAI program chairman, in extending the opportunity to produce this timely panel discussion The panelists included Dr Jack Williams. His presentation pinpointed the world forces of change, the government role in fostering efficient technological innovation, and the need to adapt to flexible manufacturing quickly. In discussing the AI industry, he said, LLThere are many similarities between AI and biotechnology, namely, the entrepreneurship and many startup firms, few products yet, but much commercial potential, a shortage of qualified talent, and a potential to create vast social change. The aspects of world forces of change are serious in that they threaten the livelihood of the U.S. economy because 70% of the U.S. output is in world markets. Abstract The consensus of government, academic, and industry leaders widely supports the strategic positioning of U.S. and Japanese research and development in mutually beneficial, two-way flows of innovation This report is derived from the IJCAI panel titled U S and Japanese Cooperation in AI and R&D Opportunities, held August 23, 1985 at the University of California at Los Angeles This panel discussed the sensitive topic of alternatives to nationalistic competitive strategies that have contributed to an extreme trade deficit surpassing $40 billion in 1985 The ideas offered by the panelists shed light on ways our countries' respective scientific communities can blend talents to achieve the best results in reducing trade frictions Each country has designated AI research as a key to unlock years of generations of technology and has directed billions of dollars to fund this development The most recognized projects are the U.S. Microelectronics Technology Computer Consortium (MCC) and Japan's Fifth Generation Computer Project (ICOT).

ARTIFICIAL INTELLIGENCE is that part of Computer Science that concerns itself with the concepts and methods of symbolic inference and symbolic representation of knowledge. But within the last fifteen years, it has concerned itself also with signals-with the interpretation or understanding of signal data. AI researchers have discussed "signal-tosymbol transformations," and their programs have shown how appropriate use of symbolic manipulations can be of great use in making signal processing more effective and efficient. Indeed, the programs for signal understanding have been fruitful, powerful, and among the most widely recog-Many different people helped in building HASP/SIAP in many different capacities. The people acknowledged below are project leaders (*), consult.ants;

Packet Radio Terminal System Evaluation Tom Ellis and Steve Saunders Work intended to result in a demonstration-level portable terminal to test and evaluate various solutions to the issues raised by extreme portability in the packet-radio environment. The Stanford Heuristic Programming Project: Goals and Activities by the Staff of the Heuristic Programming Project The Heuristic Programming Project (HPP) of the Stanford University Computer Science Department is a laboratory of about fifty people-faculty, staff, and graduate studentswhose main goals are these: ...to model, and thereby to gain a deep understanding of, the nature of scientific reasoning processes in various types of scientific problems, and various areas of science and medicine; ...as part of the methodology, and as a coordinate activity, to construct "Expert Systems"-programs that achieve high levels of performance on tasks that normally require significant human expertise for their solutidn; the HPP therefore has a natural applications orientation. The HPP was started by Professor Edward A. Feigenbaum and Professor Joshua Lederberg (now President, Rockefeller University) as the DENDRAL project in 1965. Professor Bruce Buchanan joined shortly thereafter, and is Co-Principal Investigator of the HPP. For its computing facilities, the HPP uses the Stanfordbased SUMEX-AIM National Resource for Applications of AI to Medicine and Biology (a pair of DEC KI-10s and a DEC 2020); and the SU-SCORE machine (a DEC 2060).

Editor: We are currently working on a project that attempts to integrate artificial intelligence and legal reasoning for the purpose of simulating judicial decision making. The project has defined legal reasoning and legal analysis-the former taking place before the latter begins. Using a historical approach with our legal system's basis founded in English common law, we attempted to examine the role of stare decisis in decision making. More extensively we examined the role of reasoning in legal analysis, relying on Wittgenstein and to some extend Hofstadter, for an explanation of the foundation of the thought behind man's reasoning process. Legal reasoning is a specialized thought process, but reasoning is generic to all processes that attempt to incorporate artificial intelligence.

An expert system used in the control room of this blast furnace controls fluctuations in furnace temperature, thereby saving significant amounts of energy and costs. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects. JTEC formed a panel of individuals from the academic and business communities to conduct this study. The primary objectives of the JTEC panel were to investigate Japanese knowledge-based systems development from both technological and business perspectives and compare progress and trends with similar developments in the United States. The panel focused on (1) applications in the business sector, (2) infrastructure and tools, (3) advanced knowledge systems development in industry, (4) advanced knowledge systems research in universities, and (5) national projects. The JTEC panel visited 19 sites during its 1-week visit to Japan in March 1992 and conferred with Japanese computer scientists and business executives both before and after the official visits. The panel visited four major computer manufacturers; eight companies that are applying expert systems to their operations; three universities; three national projects; and Nikkei AI, a publication that conducts an annual survey of expert system applications in Japan. This article summarizes the findings of the panel in each of the five areas listed. The panel members were Edward Feigenbaum, Stanford University (chair); Peter Friedland, National Aeronautics and Space Administration; Bruce B. Johnson, Andersen Consulting; H. Penny Nii, Stanford; Herbert Schorr, University of Southern California; and Howard Shrobe, Massachusetts Institute of Technology and Symbolics, Inc.). Robert Engelmore served as an ex officio member of the panel with the responsibility of producing the final report. Also present on the site visits were Y. T. Chien, National Science Foundation, and R. D. Shelton, JTEC. The sponsors of the JTEC study defined the dimensions of the study to include the following areas: (1) business-sector applications of expert systems; (2) advanced knowledgebased systems in industry; (3) advanced knowledge-based systems research in universities; (4) work at government laboratories, especially the laboratory of the Japanese Fifth-Generation Computer Project; and (5) the electronic dictionary research knowledge base building effort. The panel was also asked to observe the fuzzy system work being done in Japan, any neural network applications that affect expert system development, and the new national project known as Real-World Computing.

Workshops The second workshop in two years on the integration of knowledge-based systems with conventional computer techniques in agriculture and natural resource management (NRM) was held 18-19 August 1989 in Detroit, Michigan, in conjunction with the Tenth International Joint Conference on Artificial Intelligence. The workshop drew scientists from the United States and Canada, working in disciplines from engineering to entomology in universities, government, and industry. Twenty-two papers were presented at the workshop, after which participants were asked to discuss several key questions about the development, delivery, and use of knowledgebased systems in solving problems in agriculture and NRM. Solving problems in agriculture and natural resource management (NRM) requires an understanding of both biological and economic systems, systems that are characteristically complex and unpredictable. Traditional computer-based approaches to problem solving in these domains-- mathematical modeling, simulation, and optimization--have had only limited success.

The Fourth International Workshop on Artificial Intelligence in Economics and Management was held in Tel-Aviv, Israel, from 8 to 10 January 1996. This article discusses the main themes presented at the workshop, including the need for multiple methods in any system designed to solve real-world problems, the differences in the effectiveness of AI versus classic analytic techniques, and the use of AI techniques to customize products. The main themes that emerged during the workshop were (1) the need to use multiple methods in any system designed to solve major realworld problems, (2) a continuing interest in comparing the effectiveness of AI solutions with classic analytic techniques, and (3) a growing use of AI techniques to customize products to suit individual consumers. As a matter of course, almost every presentation at the workshop touched on AI techniques in one way or another. However, a group of papers at the workshop had AI techniques as their main focus.