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.

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."

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.

Efficiently entering information into a computer is key to enjoying the benefits of computing. This paper describes three intelligent user interfaces: handwriting recognition, adaptive menus, and predictive fillin. In the context of adding a person's name and address to an electronic organizer, tests show handwriting recognition is slower than typing on an on-screen, soft keyboard, while adaptive menus and predictive fillin can be twice as fast. This paper also presents strategies for applying these three interfaces to other information collection domains.

A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. 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. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. Thus, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves to survive decades of autonomous operation. 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.

Microelectronics supplier TRW optimizes the combustion process Clothing manufacturer Wrangler (Redondo Beach, CA) is using virtual in a coal-fired utility boiler, (Greensboro, NC) has developed a reality (VR) to decontaminate nuclear reducing nitrogen oxide emissions neural network system to improve facilities. The company has developed and loss on ignition while improving production planning and forecasting. An applications to its 36,000 Group (Washington, DC) has expert system makes recommendations employees worldwide. Pacific Gas & Electric (PG&E) (San provides real-time restoration of NeuralWare (Pittsburgh, PA), a Francisco, CA), a public utility, has telecommunications services in areas provider of neural network software, affected by disaster or accidents. The system allows PG&E outage through a series of tests, 24 for target and path optimization to offer customers flexible energy hours a day, 7 days a week.

In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using syntheticdata. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system -- comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment -- is manifested as an out-of-bound parameter elsewhere in the system. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. This article describes a diagnostic tool (that is, AGETS MBR), designed to overcome this problem by isolating failures using an overall system troubleshooting approach. AGETS MBR was developed jointly by personnel at Pratt & Whitney and United Technologies Research Center using an AI tool called the qualitative reasoning system (QRS).

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator. In addition, we developed the paneled-block assembly shop scheduler and the long-range production planner. For this large-scale project, we devised a phased development strategy consisting of three phases: (1) vision revelation, (2) data-dependent realization, and (3) prospective enhancement. The DAS systems were successfully launched in January 1994 and are actively being used as indispensable systems in the shipyard, resulting in significant improvement in productivity and visible and positive effects in many areas.

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system -- comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment -- is manifested as an out-of-bound parameter elsewhere in the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. This article describes a diagnostic tool (that is, AGETS MBR), designed to overcome this problem by isolating failures using an overall system troubleshooting approach. AGETS MBR was developed jointly by personnel at Pratt & Whitney and United Technologies Research Center using an AI tool called the qualitative reasoning system (QRS).