Open Mind Tree

"Computers can be programmed with rules to use information to make simple decisions. This is knowledge that has been passed on from the programmer. A simple example of this is a spreadsheet application that monitors pupils' test marks and calculates average scores. A more sophisticated example of this is an expert system. This is where computers are programmed to accept a large number of items of information and, based on rules set in the program, make decisions, then further decisions. The best-known examples of these are automatic pilots in aeroplanes and diagnosis applications used to help doctors. In both cases these systems are only as good as the rules programmed by the human computer programmer and cannot deal with the unexpected. They need to be used as aids to human decision making only. The actual doctor must confirm a diagnosis and treatment suggested by an expert system."

AI in Australia and New Zealand. By the Australian Computer Society National Committee for AI. IEEE Intelligent Systems (July/August 2004). "Australian industry plays a role in AI research, too. The Computer Sciences Corporation (previously The Continuum Company), for example, has made significant contributions. Of the various expert systems the CSC developed in the late nineties, COLOSSUS is still widely used by several major Australian insurance companies. In fact, COLOSSUS, which helps insurance adjusters assess personal injury claims, has been a worldwide success for CSC. The COLOSSUS project began in 1989 with merely an in-house system to process a huge volume of backlog claims at GIO Australia. It has since grown to multiple business units in CSC, offering different versions for the US, UK, and Australian markets. The system can handle third-party general-damages and workers-compensation claims and has penetrated much of the US market. In Australia, Trowbridge also uses COLOSSUS for their statistical study on claims data."

Developing and Deploying Knowledge on a Global Scale. By James Borron, David Morales, and Philip Klahr. AI Magazine17(4): Winter 1996, 65-76. "Reuters is a worldwide company focused on supplying financial and news information to its more than 40,000 subscribers around the world. To enhance the quality and consistency of its customer- support organization, Reuters embarked on a global knowledge development and reuse project. The resulting system is in operational use in North America, Europe, and Asia. The system supports 38 Reuter products worldwide. This article presents a case study of Reuter experience in putting a global knowledge organization in place, building knowledge bases at multiple distributed sites, deploying these knowledge bases in multiple sites around the world, and maintaining and enhancing knowledge bases within a global organizational framework. This project is the first to address issues in multicountry knowledge development and maintenance and multicountry knowledge deployment. These issues are critical for global companies to understand, address, and resolve to effectively gain the benefits of global knowledge systems."

Rule-Based Expert Systems --The MYCIN Experiments of the Stanford Heuristic Programming Project. Bruce G. Buchanan and Edward H. Shortliffe, editors (1984). Reading, MA: Addison-Wesley. The entire book is now available online from AAAI's Classic Books in AI collection. "Artificial intelligence, or AI, is largely an experimental science -- at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments." The definitive source for information on Mycin and Emycin, and the technology of building rule-based expert systems.

A Different Kind of Laboratory Mouse. By Grant Buckle. DigitalJournal.com (November 20, 2004). "It is possible to find viable alternatives to tests on live animals and, thanks to technology, at least some of them can saved without abandoning important research. ... In silico testing is an example of how technology continues to successfully create beneficial methods because once a model has such data, it may be able to predict the likely effects of chemicals and drugs without testing on live animals. But tests using computer models are still relatively new, so they’re not yet sufficient for making final decisions about the safety of drugs or chemicals for human consumption. The good news, though, is that if pre-screening with computer models determines that a compound is likely to be dangerous, the developer can decide not to pursue it further, saving time and money. ... A handful of software packages exist for doing in silico testing. ... Lhasa Ltd., a spinoff of the chemistry department of the University of Leeds in England, developed Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows, a knowledge-base expert system that analyzes the structure of chemicals and predicts whether they will be toxic. ... Computer models are still not good enough to be used as the only means of testing new drugs and chemicals, but with the ballooning growth of technology, never say never. As artificial intelligence improves, and science sees a few more breakthroughs in the way the models are developed, it might not be that far off."

Expert Systems: How Far Can They Go? Part One. By Randall Davis. AI Magazine 10(1): Spring 1989, 61-67 - and - Expert Systems: How Far Can They Go? Part Two. By Randall Davis. AI Magazine 10(2): Summer 1989, 65-77. "A panel session at the 1985 International Joint Conference on artificial intelligence in Los Angeles dealt with the subject of knowledge-based systems; the session was entitled "Expert Systems: How Far Can They Go?" The panelists included Randall Davis (Massachusetts Institute of Technology); Stuart Dreyfus (University of California at Berkeley); Brian Smith (Xerox Palo Alto Research Center); and Terry Winograd (Stanford University), chairman. The article begins with Winograd's original charge to the panel, followed by lightly edited transcripts of the panel's remarks. Part 1 includes presentations from Winograd and Dreyfus. Part 2, which will appear in the Summer 1989 issue, includes presentations from Smith and Davis and concludes with the panel discussion. Although three years have passed since this session took place, the issues raised and the points discussed are no less relevant today."

A Personal View of Expert Systems: Looking Back and Looking Ahead. By Edward A. Feigenbaum. "This was an acceptance sppech for the Feigenbaum Medal presented at the World Congress on Expert Systems at Orlando, Florida, December 1991." Available in several formats from CiteSeer.

Expertise in Context: Human and Machine. Edited by Paul J. Feltovich, Kenneth M. Ford, and Robert R. Hoffman. AAAI Press. "Computerized 'expert systems' are among the best known applications of artificial intelligence. But what is expertise? The nature of knowledge and expertise, and their relation to context, is the focus of active discussion --- even controversy --- among psychologists, philosophers, computer scientists, and other cognitive scientists. The questions reach to the very foundations of cognitive theory --- with new perspectives contributed by the social sciences. These debates about the status and nature of expert knowledge are of interest to and informed by the artificial intelligence community --- with new perspectives contributed by 'constructivists' and 'situationalists.' The twenty-three essays in this volume discuss the essential nature of expert knowledge, as well as such questions such as how 'expertise' differs from mere 'knowledge,' the relation between the individual and group processes involved in knowledge in general and expertise in particular, the social and other contexts of expertise, how expertise can be assessed, and the relation between human and computer expertise."

Life Cycle of a Multi-Expert Computer System by Nicholas Zendelbach. PCAI 17.6 This article introduces a new paradigm to the discipline of engineering human knowledge, one that we divide into four tenets of knowledge representation:

Worldwide Perspectives and Trends in Expert Systems. By Jay Liebowitz. AI Magazine 18(2): Summer 1997, 115-119. "Some people believe that the expert system field is dead, yet others believe it is alive and well. To gain a better insight into these possible views, the first three world congresses on expert systems (which typically attract representatives from some 45-50 countries) are used to determine the health of the global expert system field in terms of applied technologies, applications, and management. This article highlights some of these findings."

An Intelligent System for Case Review and Risk Assessment in Social Services. By James R. Nolan. AI Magazine 19(1): Spring 1998, 39-46. "This article reports on the development and implementation of DISXPERT, an intelligent rule-based system tool for referral of social security disability recipients to vocational rehabilitation services."

LAPS: Cases to Models to Complete Expert Systems. By Joseph S. di Piazza and Frederick A. Helsabeck. AI Magazine 11(3): Fall 1990, 80-107. A unique program for interviewing experts that interweaves knowledge gathering, organizing, and testing.

Technology, Work and the Organization: The Impact of Expert Systems. By Rob Weitz (1990). AI Magazine 11(2): Summer, 1990, 50-60.