I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In particular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rulebased approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. Traditionally, industrial diagnostic systems have been handcrafted to reflect the knowledge of a domain expert. They take the form of if-then rules that associate certain forms of abnormal system behavior with faults that could have caused this behavior.

In 1980, Digital Equipment Corporation began to use a rule-based system called Rl by some and XCON by others to configure VAX-11 computer systems In the intervening years, Rl's knowledge has increased substantially and its usefulness to Digital continues to grow. This article describes what is involved in extending Rl's knowledge base and evaluates Rl's performance during the four year period. "Rl: the formative years" described how a A large number of people have played critical roles in Rl's development. Among those who deserve special mention are John Barnwell, Dick Caruso, Ken Gilbert, Keith Jensen, Allan Kent, Dave Kiernan, Arnold K&t, Dennis O'Connor, and Ed Orciuch. We want to thank Allen Newell, Dennis O'Connor, and Ed Orciuch for their helpful comments on earlier drafts of this article Briefly, given a customer's purchase order, Rl determines what, if any, substitutions and additions have to be made to the order to make it consistent, complete, and produce a number of diagrams showing the spatial and logical relationships among the 50 to 150 components that typically constitute a system.

In this editorial we introduce the articles published in this special AI Magazine issue on innovative applications of artificial intelligence. Discussed are a pick-pack-and-ship warehouse-management system, a neural network in the fishing industry, the use of AI to help mobile phone users, building business rules in the mortgage lending business, automating the processing of immigration forms, and the use of the semantic web to provide access to observational datasets. Pop culture often shows AI in clearly visible forms like a futuristic robot attempting to take over the earth. However, current applications of AI are often more difficult to detect because they are imbedded in existing products, processes, and services. We interact with and benefit from these AI applications in ways that most people never even notice.

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict This model partitions an intelligent system into two asynchronous components The Interviewer's primary function is to gather data while providing an acceptable response time to the user The Reasoner does most of the symbolic computation for the system This paper describes the implementation of the model in both timesharing and personal workstat,ion environments, and uses the ONCOCIN system as an example The work described in t,his paper was carried out at Stanford University and was partly supported by the National Library of Medicine under program project grant LM-00395. The original idea for splitting the tasks of information gathering from reasoning in order to improve system response time was suggested by Ted Shortliffe and Chuck Clanton for the ONCOCIN project Thanks are due to Eric Schoen and Bill van Melle for help with the implementation, to Mark Stefik and Harold Brown for help in writing this paper, and to the rest of the ONCOCIN project members, including Carli Scott, Miriam Bischoff, Charlotte Jacobs, and Craig Tovey. An acceptable response time is needed both during system testing and to help insure end-user acceptability. During the normal course of development of an AI system there is substantial t,esting on real problems under the guidance of human experts whose time is usually valuable. Moreover, many end users (e.g., physicians) will simply refuse to use a system if they have to wait for a response.

J. Sainsbury has extensive assets, with subsidiaries such as Shaws in the United States and the Savacentre and Homebase chains in the United Kingdom. Given J. Sainsbury's position in the retail market, the efficient and effective running of the supply chain for J. Sainsbury is critical to the mission of the organization. The J. Sainsbury logistics purpose statement is to manage the flow of goods from supplier to shelf, ensuring that the customer has the right product in the right place at the right time. To these ends, J. Sainsbury's Logistics Group is committed to being world class. The group's direction principle is to be seen as the world's best logistics team.

Efficient indexing schemes have influenced the acceptance of production systems in the industrial world. However, in embedded-control systems, production systems have not been applied intensively because of their nondeterministic run-time behavior. Thus, nonpredictability of response times is a major obstacle to the widespread use of expert systems in the real-time domain. Such systems are considered intelligent when they are able to perform complex actions in response to the sensed environment. In intelligent real-time systems, there is a tradeoff between acting and reasoning.

Artificial Intelligence is solving more and more real world problems, but penetration into the complexities of government administration has been minimal. The author suggests that combining expert system technology with conventional procedural computer systems can lead to substantial efficiencies. Business rules can be removed from business-oriented computer systems and stored in a separate but integrated knowledge base, where maintenance will be centralized. Fourteen specific practical applications are suggested. Traditionally, these systems have been used to automate the accounting function, automate labor-intensive activities, manage and control vast financial and physical assets, process payrolls for hundreds of thousands of employees, and merge and summarize information about a wide set of activities in support of management decision making.

Southwest Research Institute and the U.S. Air Force Materiel Command designed and developed an automated system for the preparation of deficiency report analysis information reports ( Engineers and equipment specialists responsible for the troublesome part, or end item, review the MDR to identify the possible cause(s) of failure. In the past, engineers and equipment specialists have turned to operations research (OR) analysts to assist in item performance analysis. This analysis is usually time consuming and personnel intensive and requires information from many Air Force data systems. At the Oklahoma City Air Logistics Center (ALC), located at Tinker Air Force Base, data collection and analysis require two person-days. This document describes an item's SOURCE DATA: The data used to prepare this report came from the following sources: 1) Product Performance Subsystem (G099), 2) Supportability analysis Forecasting Evaluation (SAFE), 3) Flying Hours (G099), 4) MICAP Hours (D165B), and 5) VAMOSC (D160B).

Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules.

Loan underwriting is the process of evaluating a loan application to determine whether the loan should be funded. The process often starts with a potential borrower walking into a branch office and requesting a loan to purchase or refinance a home. A processor asks the borrower to fill out an application, setting in motion a lengthy information-gathering process in which as many as 1500 data-element pieces will eventually be collected. This loan information includes items about the borrower's employment, income, assets, liabilities, and monthly expenses. During the process, a credit report and appraisal will be ordered from a third-party vendor.