Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods that allow the identification, characterization, and prediction of the variability of oil-field cements. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than 10 percent. Composition and particle-size distributions can be predicted with uncertainties a little greater than measurement error, but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques, which provide the basis for a valuable quality control tool now finding commercial use in the oil field.

Topics An all-star panel assembled to pose life, and a wealth of other topics were of the technical papers spanned a "Challenge Problems for Artificial Intelligence."

As part of a collaboration with the White House Office of Media Affairs, members of the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology designed a system, called COMLINK, that distributes a daily stream of documents released by the Office of Media Affairs. Approximately 4,000 direct subscribers receive information from this service, but more than 100,000 people receive the information through redistribution channels. The information is distributed through e-mail and the World Wide Web. In such a large-scale distribution scheme, there is a constant problem of subscriptions becoming invalid because the user's e-mail account has terminated. These invalid subscriptions cause a backwash of hundreds of bounced-mail messages each day that must be processed by the operators of the COMLINK system. To manage this annoying but necessary task, an expert system named BMES was developed to diagnose the failures of information delivery.

As a field, knowledge representation has often been accused of being off in a theoretical no-man's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR-92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93).

This column, like many strange things in the modern world, was conceived in an email exchange. Someone said to an editor: "why not have a regular lighthearted column on AI topics?" The editor said: "what an excellent idea, and when will we get the first manuscript?" and the first person said: "oh but I didn't volunteer;" and the editor said: "listen, buddy, I can make your life very uncomfortable if I don't get some cooperation. We go to press next week." While looking for something to give him, we stumbled on this old manuscript, written years ago (with our esteemed colleague Neil Agnew, the Duke of York). Ever had an old sock that you try to throw away, but keep finding in the bottom of a drawer? This is a bit like that. Come to think of it, so is the frame problem. Anyway, you can't make an omelette without breaking eggs, so here is our first reflection. It's a variation on an old, old story ....

Mach-5 aircraft concept jointly funded IntelliCorp (Mountain View, Calif.), a by the National Aeronautics and vendor of knowledge-based live modeling ZEBCO MotorGuide (Tulsa, Okla.), a Space Administration and the United software, has joined PDES, a fishing tackle manufacturer, has implemented States Air Force. The aircraft utilizes global consortium working to accelerate speech-recognition technology neural technology to improve maneuverability, the development and deployment to develop a voice-activated reliability, and efficiency. of the standard for the exchange of control system for fishing boats.

Reinforcement learning has become one of the most actively studied learning frameworks in the area of intelligent autonomous agents. This article describes the results of a three-day meeting of leading researchers in this area that was sponsored by the National Science Foundation. Because reinforcement learning is an interdisciplinary topic, the workshop brought together researchers from a variety of fields, including machine learning, neural networks, AI, robotics, and operations research. Thirty leading researchers from the United States, Canada, Europe, and Japan, representing from many different universities, government, and industrial research laboratories participated in the workshop. The goals of the meeting were to (1) understand limitations of current reinforcement-learning systems and define promising directions for further research; (2) clarify the relationships between reinforcement learning and existing work in engineering fields, such as operations research; and (3) identify potential industrial applications of reinforcement learning.

This article discusses the First International Symposium on Integrating Knowledge and Neural Heuristics, held on 9 to 10 May 1994 in Pensacola, Florida. The highlights of the event are summarized, organized according to the five areas of concentration at the conference: (1) integration methodolo-gies; (2) language, psychology, and cognitive science; (3) fuzzy logic; (4) learning; and (5) applications.

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.

An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. Accounting sys-ems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. To cope with this complexity and variability, the COMET system applies a model-based reasoning approach to the analysis of accounting systems and their controls. An auditor uses COMET to create a hierarchical flowchart model that describes the intended processing of business transactions by an accounting system and the operation of its controls. COMET uses the constructed model to automatically analyze the effectiveness of the controls in detecting potential errors. Price Waterhouse auditors have used COMET on a variety of real audits in several countries around the world.