The primary goal of expert systems research is to make expertise available to decision makers and technicians who need answers quickly. There is never enough expertise to go around -- certainly it is not always available at the right place and the right time. Portable with computers loaded with in-depth knowledge of specific subjects can bring decades worth of knowledge to a problem. The same systems can assist supervisors and managers with situation assessment and long-range planning. Many small systems now exist that bring a narrow slice of in-depth knowledge to a specific problem, and these provide evidence that the broader goal is achievable.

These knowledge-based applications of artificial intelligence have enhanced productivity in business, science, engineering, and the military. With advances in the last decade, today's expert systems clients can choose from dozens of commercial software packages with easy-to-use interfaces.

Each new deployment of an expert system yields valuable data for what works in what context, thus fueling the AI research that provides even better applications.

Good Places to Start

Expert Systems - Make a Diagnosis. Part of It's Alive! - From airport tarmacs to online job banks to medical labs, artificial intelligence is everywhere. By Jennifer Kahn. Wired Magazine (March 2002/ Issue 10.03). "Intuition may seem like a human trick, but machines can be pretty good at it, too. Underlying a hunch are dozens of tiny, subconscious rules - truths we've learned from experience. Add them up and you get instinct: a doctor's sense that a patient's stomachache might really be appendicitis, for example. Program those rules into a computer and you get an expert system - one of many that can screen lab tests, diagnose blood infections, and identify tumors on a mammogram."

Expert Systems. Section 1.2.3 of Chapter One (available online) of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005)."One major insight gained from early work in problem solving was the importance of domain-specific knowledge. A doctor, for example, is not effective at diagnosing illness solely because she possesses some innate general problem-solving skill; she is effective because she knows a lot about medicine. Similarly, a geologist is effective at discovering mineral deposits because he is able to apply a good deal of theoretical and empirical knowledge about geology to the problem at hand. Expert knowledge is a combination of a theoretical understanding of the problem and a collection of heuristic problem-solving rules that experience has shown to be effective in the domain. Expert systems are constructed by obtaining this knowledge from a human expert and coding it into a form that a computer may apply to similar problems. This reliance on the knowledge of a human domain expert for the system's problem solving strategies is a major feature of expert systems. "

Introduction to Expert Systems. From expertise2go.com. "This tutorial shows you how a computer-based expert system emulates the behavior of a human advisor, presents terminology unique to the field and introduces the activities that must be accomplished to build expert systems."

• Don't miss their Expert System Glossary and the rest of their tutorials and demos.

Expert Systems and Artificial Intelligence. Part of the Introduction by Robert S. Engelmore and Edward Feigenbaum for the May 1993 Japanese Technology Evaluation Center panel's report about Knowledge-Based Systems in Japan, and now available from the World Technology Evaluation Center (WTEC). Topics covered include "The Building Blocks of Expert Systems" ("Every expert system consists of two principal parts: the knowledge base; and the reasoning, or inference, engine.") and "Knowledge Engineering" ("[T]he art of designing and building expert systems, and knowledge engineers are its practitioners".).

• Be sure to check out the Table of Contents for additional information including, Expert Systems Building Tools: Definitions, by H. Penny Nii: "An expert system tool, or shell, is a software development environment containing the basic components of expert systems. Associated with a shell is a prescribed method for building applications by configuring and instantiating these components."

Artificial intelligence gets real. By Daniel Lyons. Forbes Global (November 30, 1998) ."By contrast, Feigenbaum succeeded by thinking small. Unlike his rivals, he didn't set out to recreate all of human intelligence in a computer. His idea was to take a particular expert -- a chemist, an engineer, a pulmonary specialist -- and figure out how that person solved a single narrow problem. Then he encoded that person's problem-solving method into a set of rules that could be stored in a computer."

Introduction to AI and Expert Systems. By Carol E. Brown and Daniel E. O'Leary. The tutorial uses summaries and outline form to explain AI, reasoning, and knowledge engineering. Examples of expert systems used in business accounting are decribed.

Readings Online

A UMLS-based Knowledge Acquisition Tool for Rule-based Clinical Decision Support System Development. By Soumeya L. Achour, MS, DVM, Michel Dojat, Eng, PhD, Claire Rieux, MD, Philippe Bierling, MD, PhD, and Eric Lepage, MD, PhD. Journal of the American Medical Informatics Association 8 (4): 351­360. July 2001. Abstract: "Decision support systems in the medical field have to be easily modified by medical experts themselves. The authors have designed a knowledge acquisition tool to facilitate the creation and maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) contains the domain entities and constitutes the relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the pre-established domain ontology and condition­action rule templates that are well adapted to several clinical decision-making processes. Corresponding medical logic modules are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on the highly progressive knowledge embedded in hospital information systems."

Bruce Buchanan Retires. Interview by John Aronis for Links, the newsletter of The Department of Computer Science at the University of Pittsburgh (Spring 2003; pages 2 - 4). "While working in the Stanford Artificial Intelligence Laboratory, Bruce and his collaborators made important contributions to artificial intelligence. Their assertion -- obvious in retrospect like most great ideas -- was that knowledge is important for intelligent behavior. They drove this point home with a series of programs that embodied the knowledge of scientific and medical experts -- sometimes rivaling or surpassing their abilities -- and the creation of an industry centered around expert systems."

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."

• Visit CSC's COLOSSUS page.

Developing and Deploying Knowledge on a Global Scale. By James Borron, David Morales, and Philip Klah (1996). AI Magazine 17 (4): 65-76.

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.

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."

• Visit LHASA Limited.

Expert Systems: How Far Can They Go? Part One. By Randall Davis. AI Magazine 10(1): 61-67 (Spring 1989)- and - Expert Systems: How Far Can They Go? Part Two. By Randall Davis. AI Magazine 10(2): 65-77 (Summer 1989) "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."

• Also see: Expert Systems: Where Are We? And Where Do We Go from Here? By Randall Davis. AI Magazine 3(2): 3-22 (Spring 1982). "Work on Expert Systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We'll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge. I guarantee we'll all return safely, so come along...."

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. The following excerpt is from the Preface which is available online: "Several disciplines share an interest in understanding the concept of expertise. In particular, the nature of intelligence and expertise are matters of significant concern to psychologists, philosophers, and various kinds of cognitive scientists. Computerized 'expert systems' form the best known applications of artificial intelligence (AI). But what is the expertise that experts (human or otherwise) can be said to have? This issue raises many other questions, and has lately given rise to considerable controversy. Some of this discussion reaches to the very foundations of cognitive theory, with new perspectives contributed by the social sciences. ... How does 'expertise' differ from mere 'knowledge?' ... The nature of knowledge is central to AI. The large number of successful applications of conventional AI technology, utilizing knowledge obtained by careful conversations with human experts, has already begun to put a strain on the classical idea that knowledge can simply be written down (perhaps with a little effort and guided introspection)."

Eliciting Knowledge and Transferring it Effectively to a Knowledge-Based System. Brian R. Gaines and Mildred L. G. Shaw, Knowledge Science Institute, University of Calgary. "[T]he elicitation of expert knowledge and its effective transfer to a useful knowledg--based system is complex and involves a diversity of activities. This paper illustrates the complete development of a decision support system using knowledge acquisition tools. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering."

Expert System computer programs for analysis of Archaeological material. By Roger Grace. Nicely illustrated explanations of the LITHAN [LITHic ANalysis of stone tools] and FAST [Functional Analysis of Stone Tools] expert sysytems.

Doctors' Orders - As expert systems become more expert, physicians see the advantages of taking on CPOE. by Mark Hagland. Healthcare Informatics (January 2003). "If the future is a clinically driven, elegantly managed, intelligent system--one that helps physicians optimize patient care and helps hospitals and medical groups constantly improve care delivery--then the folks at Ohio State University (OSU) Health System, Columbus, have at least glimpsed the future. They're in the vanguard of patient care organizations using computerized physician order entry (CPOE) systems which, authorities say, best represent the legacy of years of expert-systems development work in healthcare."

LifeCode: A Deployed Application for Automated Medical Coding. By Daniel T. Heinze, Mark Morsch, Ronald Sheffer, Michelle Jimmink, Mark Jennings, William Morris, and Amy Morsch. AI Magazine 22(2): 76-88 (Summer 2001). This paper is based on the authors' presentation at the Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000). "LifeCode is a natural language processing (NLP) and expert system that extracts demographic and clinical information from free-text clinical records."

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."

Expert Systems. Section 1.2.3 of Chapter One (available online) of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005). "One major insight gained from early work in problem solving was the importance of domain-specific knowledge. ... Expert knowledge is a combination of a theoretical understanding of the problem and a collection of heuristic problem-solving rules that experience has shown to be effective in the domain. Expert systems are constructed by obtaining this knowledge from a human expert and coding it into a form that a computer may apply to similar problems. This reliance on the knowledge of a human domain expert for the system's problem solving strategies is a major feature of expert systems. Although some programs are written in which the designer is also the source of the domain knowledge, it is far more typical to see such programs growing out of a collaboration between a domain expert such as a doctor, chemist, geologist, or engineer and a separate artificial intelligence specialist."

New Tools Help Hospitals Handle Terror Attacks And Other Disasters. By Marianne Kolbasuk McGee. InformationWeek (April 14, 2005). "When hospitals deal with a disaster, whether treating dozens of casualties from a serious highway pileup or hundreds of potential terrorist-attack victims, emergency workers and hospital administrators rely predominately on ringed binders containing hundreds of pages of emergency instructions and procedures. ... To help make disaster management more efficient, health-care purchasing group Amerinet is making available to its 1,800 hospital members a new interactive, Web-based disaster-management system developed by PortBlue Corp., a maker of expert-system software. PortBlue's new Hospital Incident Response System helps hospital workers deal with smaller-scale crises, such as an internal fire; larger disasters, like plane crashes; and potential national emergencies, such as biological or chemical attacks, PortBlue CEO and founder Paul Dimitruk says."

• Also see: What are Expert Systems? and Knowledge Management Overview from PortBlue.

The F-16 Maintenance Skills Tutor. By Christopher Marsh. The Edge - The MITRE Advanced Technology Newsletter (March 1999). "The F-16 Maintenance Skills Tutor simulates the experience of on-the-job training including running tests, moving switches, replacing components, taking measurements, and asking an expert for help. This is done by giving the student realistic guided simulations of real-life problem situations using software models and high resolution graphics backed by an expert system that knows how to troubleshoot. This gives the student an experience that is progressive (easier problems are given to the student first). It provides explanations and help (when requested by the student), and it gives the student practice in the mechanics of expert problem solving."

An Expert System Using Nonmonotonic Techniques for Benefits Inquiry in the Insurance Industry. Leora Morgenstern and Moninder Singh. In Proceedings Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann (1997). Abstract: "Describes BenInq, an expert system used in the medical insurance industry by both customer service representatives, who answer questions about the extent of a patient's coverage for medical care, and policy modifiers, who frequently change coverage rules. Reasoning is performed by inheriting business rules, represented as formulae of first-order logic, in a semantic network in which formulae are attached to the nodes."

Stanford Medical Informatics: Uncommon research, common goals. By Mark A. Musen. MD Computing (January/February 1999). "DENDRAL demonstrated the power of encoding large amounts of domain knowledge for use by an automated reasoning system, and offered one of the first examples of rule-based programming."

• Also see their collection: "Historical Projects - A number of major projects [DENDRAL, META-DENDRAL, MYCIN, TEIRESIAS, EMYCIN....] are now considered completed work; their goals have been met, and our research attention has moved on to new areas. Major references for the projects described briefly here are listed by project in the section Further Readings."

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."

An Expert System for Automotive Diagnosis. By Jeff Pepper. From Ray Kurzweil's book, The Age of Intelligent Machines (1990). "This 'expert in a box' will guide a human technician through the entire service process, from the initial customer interview at the service desk to the diagnosis and repair of the car in the garage."

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

Expert Systems in Agriculture. Science Tech Entrepreneur (May 2002). "The application of expert system technology to agriculture seems natural, considering the widespread use of extension agents in the field. Aid from experts, who have encoded their knowledge in computer programs, may help alleviate some of the problems in agriculture. These software programs typically fit into the category of decision support tools. ... Although there is no general standard for expert systems, most include : * a knowledge base of domain facts and associated heuristics * an inference procedure or control structure for utilizing the knowledge base * a natural language user interface. ... The expert system is designed to answer questions typed at a keyboard attached to a computer on such diversified topics, for example, in pest control, the need to spray, selection of a chemical to spray, mixing and application, optimal machinery management practices, weather damage recovery such as freeze, frost or drought, etc." Several expert systems (including Grain Marketing Advisor, POMME, and SOYEX) are described.

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

Related Web Sites

"Berkeley Expert Systems Technology (BEST) lab is an Artificial Intelligence, Expert Systems and Information Technologies laboratory in the Department of Mechanical Engineering at University of California at Berkeley."

CLIPS. Maintained by Gary Riley. "One of the results of research in the area of artificial intelligence has been the development of techniques which allow the modeling of information at higher levels of abstraction. These techniques are embodied in languages or tools which allow programs to be built that closely resemble human logic in their implementation and are therefore easier to develop and maintain. These programs, which emulate human expertise in well defined problem domains, are called expert systems. The availability of expert system tools, such as CLIPS, has greatly reduced the effort and cost involved in developing an expert system. Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, or 'rules of thumb,' which specify a set of actions to be performed for a given situation. A rule is composed of an if portion and a then portion. ... The origins of the C Language Integrated Production System (CLIPS) date back to 1984 at NASA's Johnson Space Center. ... CLIPS is now maintained independently from NASA as public domain software." -excerpt from, What is CLIPS?

Demos - an eclectic mini-collection:

• Expert System demos from Acquired Intelligence, Inc.: 1) "The Whale Watcher expert system combines artificial intelligence, marine biology and web technology to produce an interactive system that helps you with whale identification." 2) "Douglas-Fir Cone and Seed Insects System ... is intended to assist seed orchard managers and cone and seed collectors, dealers and researchers in the identification of insects associated with Douglas fir cones." and 3) "The Graduate Admissions Screening System demonstrates the use of ACQUIREš with an administrative screening task - the categorization of student applications for admission to graduate school."
• Knowledge Automation Expert System demos and case studies from Exsys: "Expert systems are more widely used now than ever before. The goal of EXSYS knowledge automation expert systems is to capture, automate and disseminate decision-making advice and recommendations."
• Medical Expert System demos from EasyDiagnosis assist with the diagnosis of Constipation and Depression.
• The Wedding Planner. From Organizers for Us. "Not every part of The Wedding Planner for Us is a virtuoso demonstration of artificial intelligence (like we use for the envelope text and etiquette rules) or statistical analysis (like we use for projecting your attendance). After all, if you're writing thank-you notes, all you really need is a list of who gave you what, so you can check off which gifts you've sent thank-yous for ... and that's exactly what we give you. For every area of your wedding planning where we think that software can help you (not just give you busy work), we provide you with the tools you need to get the job done, as quickly and simply as possible." A free trial download is available.
  • Also see their press release: Artificial Intelligence Meets Ancient Ritual in New Wedding Planning Software (September 23, 2004 / available from PR Web). "Organizers for Us ... is now releasing a product that uses advanced artificial intelligence to allow their software to understand ­ of all things ­etiquette for weddings! 'It might seem strange to use these super-advanced techniques to produce software that can think like an old-fashioned lady from your local church, but we think our customers are going to love it,' says company president Stacy Smyth. 'After all, why has tax preparation software become so popular? Because taxes involve a bunch of complicated rules that everybody wants to get right, but very few people want to learn. For a lot of our customers, the formal etiquette used to word invitations, address envelopes, and all the rest of the process is very much the same way.' The program uses artificial intelligence (A.I.) for a lot of different aspects of etiquette, but one of the best examples is the 'expert system' that automatically creates the wording for invitations. The wedding planning software asks the user questions about their wedding plans, and then, based on the specific situation (A couple hosting their own wedding, which will be outside, at the residence of a friend, at a particular time, etc.) the software use the classic, formal rules of etiquette to produce invitation wording which takes into account all of the factors, and is still elegant and proper."

Expert System Projects from AIAI, the Artificial Intelligence Applications Institute at the University of Edinburgh's School of Informatics. Projects include: Formation - "A knowledge-based document layout system now in use in the production of the British Telecom Yellow Pages," and EASE for Windows - "A knowledge-based system for assessing workplace exposure to potentially hazardous new substances."

Expert Systems. From PC AI Magazine / Knowledge Technology, Inc. In addition to an short overview and a glossary of terms, you'll find links to commercial vendors, academic research groups, articles, books, and more!

Expert Systems. From Webopedia.

International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems: sponsored by ISAI, the International Society of Applied Intelligence.

• The 18th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE 2005).
• The 17th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE 2004).
• Proceedings are available from the ACM Portal.

The Joshua Lederberg Papers: Artificial Intelligence and Expert Systems.

The Joshua Lederberg Papers, part of the National Library of Medicine's Profiles in Science archival collection, contains a wealth of information about DENDRAL, "a prototype for expert systems and the first use of artificial intelligence in biomedical research."

• Overview: Computers, Artificial Intelligence, and Expert Systems in Biomedical Research. Excerpt: "The immediate impetus for Lederberg's research into biomedical applications of computers came from his participation in the National Aeronautics and Space Administration's Mars missions from 1961 onward, for which he designed a computer-controlled mass spectrometer capable of analyzing the Martian surface for signs of life. Lederberg soon applied the theoretical principles of computerized spectrometry to experimentation in the chemical laboratory, where, in 1965, they became the foundation of DENDRAL, a prototype for expert systems and the first use of artificial intelligence in biomedical research. DENDRAL (for Dendritic Algorithm) was a computer program devised by Lederberg, chairman of the Stanford computer science department Edward A. Feigenbaum, and chemistry professor Carl Djerassi for the elucidation of the molecular structure of unknown organic compounds taken from known groups of such compounds, such as the alkaloids and the steroids. ... Lederberg and his colleagues believed that artificial intelligence--the use of computers for manipulating symbols, for instance the combination of words in an 'if-then' inference, rather than for purely numerical calculation--could assimilate the rules of inductive reasoning and empirical judgment that guide scientists and physicians in their work, rules for which mathematical representations did not exist. Bruce Buchanan and others in Stanford's computer science department distilled these rules, which they called 'heuristics,' from extended interviews with Lederberg and other experts in their respective fields, and translated them into the formal code of symbolic computation."
• How DENDRAL was conceived and born. Typescript of Lederberg's November 5, 1987 talk at the Association for Computing Machinery Symposium on the History of Medical Informatics."As agreed with your organizers, this will be a somewhat personal history. They have given me permission to recall how I came to work with Ed Feigenbaum on DENDRAL, an exemplar of expert systems and of modelling problem-solving behavior. My recollections are based on a modest effort of historiography, but not a definitive survey of and search for all relevant documents. On the other hand, they will give more of the flow of ideas and events as they happened than is customary in published papers in scientific journals...."
• Also check out the Lederberg oral history video which is just one of our many Interviews & Oral History links.

Related Pages

• AI at your service
• Applications
• General Index to AI in the news: Expert Systems [a great place to find recent applications]
• Intelligent Tutoring Systems
• Knowledge Management
• Software

More Readings

Abdelguerfi, Mahdi, and Simon H. Lavington. 1995. Emerging Trends in Database and Knowledge-Base Machines: The Application of Parallel Architectures to Smart Information Systems. Los Alamitos, CA: IEEE Computer Society Press. Awad, Elias. 1996. Building Expert Systems: Principles, Procedures and Applications. Cambridge: Course Technology.

Buchanan, Bruce G., and Reid G. Smith. 1988. Fundamentals of Expert Systems. In Annual Review of Computer Science, Vol. 3, ed. Traub, Joseph F., Barbara J. Grosz, Butler W. Lampson, et al., 23-58. Palo Alto, CA: Annual Reviews, Inc.

Castillo, Enrique, Jose M. Gutierrez, and E. Castillo. 1996. Expert Systems and Probabilistic Network Models. New York: Springer Verlag.

Durkin., John 1994. Expert Systems : Design and Development. New York: Maxwell Macmillan International. Beginners will find the overview in Chapter 1 especially useful, along with the appendix of expert systems applications, listed and summarized by field (agriculture, business, education, and much more). Sections of the book are quite technical.

Feigenbaum, Edward A., Pamela McCorduck, and H. P. Nii. 1988. The Rise of the Expert Company: How Visionary Companies are Using Artificial Intelligence to Achieve Higher Productivity and Profits. New York: Times Books.

Hayes-Roth, Frederick, and Neil Jacobstein. 1994. The State of Knowledge-Based Systems. Communications of the ACM 37 (3): 27-39.

Jackson, Peter. Forthcoming. Introduction to Expert Systems, 3rd edition. London: Longman Addison Wesley. (Or see 2nd edition, 1986.)

Liebowitz, Jay. 1997. Handbook of Applied Expert Systems. Boca Raton, FL: CRC Press.

Mahesh, Kavi, and Sergei Nirenburg. 1997. Knowledge-Based Systems for Natural Language. In The Computer Science and Engineering Handbook, ed. Allen B. Tucker, Jr., 637-653. Boca Raton, FL: CRC Press, Inc.

Mann, Charles K., and Stephen R. Ruth, editors. 1992. Expert Systems in Developing Countries : Practice and Promise. Boulder, CO: Westview Press.

McDermott, J. 1982. A Rule-Based Configurer of Computer Systems. Artificial Intelligence 19 (1): 39-88.

Phelps, R., F. Ristor, D. Mukherjee, et al. 1991. INCA-An Innovative Approach to Constructing Large-Scale Real-Time Expert Systems. In Innovative Applications of Artificial Intelligence 2, ed. Rappaport, Alain and Reid Smith, 3-14. Menlo, Ca: AAAI

Rich, Elaine, and Kevin Knight. 1991. Expert Systems. In Artificial Intelligence, New York: McGraw Hill. Chapter 20 gives a ten page overview of expert systems.

Stefik, Mark. 1995. Introduction to Knowledge Systems, San Francisco: Morgan Kaufmann. Beginners will find the Chapter 3 overview of expert systems especially useful, and will want to look at some of the other sections on symbol-level, knowledge-level, troubleshooting, and more.

Talebzadeh, Houman and Sanda Mandutianu, and Christian F. Winner. 1995. Countrywide Loan-Underwriting Expert System. AI Magazine 16(1): 51-64. "Countrywide loan-underwriting expert system (clues) is an advanced, automated mortgage-underwriting rule-based expert system. The system was developed to increase the production capacity and productivity of Countrywide branches, improve the consistency of underwriting, and reduce the cost of originating a loan. The system receives selected information from the loan application, credit report, and appraisal. It then decides whether the loan should be approved or whether it requires further review by a human underwriter. If the system approves the loan, no further review is required, and the application is funded. clues has been in operation since February 1993 and is currently processing more than 8500 loans each month in over 300 decentralized branches around the country."

Torsun, I. S. 1995. Foundations of Intelligent Knowledge-Based Systems. New York: Academic Press.

Turban, Efraim, and Jr. Louis E. Frenzel 1992. Expert Systems and Applied Artificial Intelligence. New York: Maxwell Macmillan International.

Walker, Terri C., and Richard K. Miller. 1990. Expert Systems Handbook : An Assessment of Technology and Applications. Englewood Cliffs, NJ: Prentice-Hall.

Weiss, Sholom M., and Casimir A. Kulikowski. 1991. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. San Mateo, CA: Morgan Kaufmann.