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. 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.

Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. 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. 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.

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. 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.

The supply-chain integrated ordering network (SCION) depot-bookings system automates the planning and scheduling of perishable and nonperishable commodities and the vehicles that carry them into J. This initiative is strategic, enabling the business to make the key move from weekly to daily ordering. The system leverages AI techniques to provide a business solution that meets challenging functional and performance needs. The SCION depot-bookings system is operational, providing schedules for 22 depots across the United Kingdom.

This article is a reflection on the goals and focus of the Innovative Applications of Artificial Intelligence (IAAI) Conference. The author begins with an historical review of the conference. He then goes on to discuss the role of the IAAI conference, including an examination of the relationship between AI scientific research and the application of AI technology. He concludes with a presentation of the new vision for the IAAI conference.

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. 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. 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.

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).

To enhance the quality and consistency of its customer- support organization, Reuters embarked on a global knowledge development and reuse project. 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.

Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. What characterizes these domains is that it is not feasible (computationally) or desirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation.

These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. Achieving these large-scale modeling and configuration tasks will require a tight coupling between the higher-level coordination function provided by symbolic reasoning and the lower-level autonomic processes of adaptive estimation and control. To be economically viable, they will need to be programmable purely through high-level compositional models. Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous system architecture that is taking us into the new millennium.