The Third International Workshop on Human and Machine Cognition was held in Seaside, Florida, on 13-15 May 1993. Each paper session included presentations on cognitive research, educational research, AI theory and logic, and particular knowledge engineering projects. This mixture encouraged the participants from diverse disciplines to listen and respond to one another. These international workshops are held to allow leading scientists, scholars, and practitioners to discuss current issues and research in particular topics in AI and cognitive science.

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. The RETE and TREAT algorithms and their offspring play a major role in the implementation of efficient pattern-matching systems.

Chevron Canada is decentralizing its computer environment from mainframes to PCs e Emission Reduction Research mounted infantry virtual environment) and Sun workstations. The DIVE belt provides the Chevron oil exploration crews will be N.J.) has developed the Batch Design ability to operate inside a virtual environment able to retrieve various types of well Kit, an expert system for optimizing without becoming tangled batch processes and minimizing pollution. Funded by the U.S. The system will help eliminate Army, the DIVE project is designed to ADVANTA Mortgage (San Diego, avoidable pollution and save pharmaceutical allow soldiers to operate within a virtual Cal.) has signed a license agreement and chemical manufacturers battlefield. VR is being used to demonstrate used as the focal point of exhibition fire engineering principles such The Santa Fe Institute (Santa Fe, stands designed by Photosound for as means of escape theory, fire modeling, N.M.) has won an ARPA grant of such pharmaceutical firms as Smith-human behavior, and spatial $323,000 for research on complex Kline Beecham. The system will be designed advanced computin arena.

The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. Machine-learning tools have contributed positively to the process of trying to eliminate or open up this bottleneck, but how do we know whether the field is progressing? How can we determine the progress made in any of its branches? How can we be sure of an advance and take advantage of it? This article proposes a benchmark as a classificatory, comparative, and metric criterion for machine-learning tools. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool. The proposed model has been applied to a set of machine-learning tools, comparing expected and obtained results. Experimentation validated the model and led to interesting results.

(Stankovic and Ramamithram 1990). Such systems are considered intelligent switching that is used in the railway stations when they are able to perform complex of three European countries. Parts of the control actions in response to the sensed environment. Time is a valuable resource that is lost when Years ago, a request came from the railway the system must reason about actions before authorities for a system that could guarantee performing them. This requirement is understandable on two levels: the problem-space level because in typical real-time systems, and the knowledge base level (Tambe and scheduling is mostly based on worstcase Newell 1988). With the problem-space level, execution times of the tasks involved. Usually, a sequence of steps is required the railway authorities.

Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.

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 (DRAIRs). A DRAIR provides Air Force engineers with an analysis of an aircraft item's performance history, including maintenance, supply, and cost. A DRAIR also recommends improvements for a deficient materiel or aircraft part. The successful design, development, and deployment of the DRAIR ADVISER system by applying a combination of knowledge-based system and database management techniques are the subject of this article.

Engineers Doing so would reduce demands on the OR and equipment specialists responsible for the analysts and provide additional time for them troublesome part, or end item, review the to address more complex analysis problems. MDR to identify the possible cause(s) of failure. Further, with the turnover of personnel in the In the past, engineers and equipment military and the aging of the aircraft fleet, specialists have turned to operations research another objective was to capture expertise (OR) analysts to assist in item performance from personnel who are most knowledgeable analysis. This analysis is usually time consuming about specific aircraft systems and federal and personnel intensive and requires stock classes (FSCs) and make this expertise information from many Air Force data systems. Center (ALC), located at Tinker Air Force Base, data collection and analysis require two person-days. This document describes an item's to the automation of 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). MAINTENANCE DATA (D056): A total of 175 inherent failures occurred between JUL 1991 and JUN 1992, which translates into a Mean Time Between Maintenance Type-1 (MTBM-1) of 162 hours.

Real-time problem solving is not only reasoning about time, it is also reasoning in time. This ability is becoming increasingly critical in systems that monitor and control complex processes in semiautonomous, ill-structured, real-world environments. Many techniques, mostly ad hoc, have been developed in both the real-time community and the AI community for solving problems within time constraints. However, a coherent, holistic picture does not exist. This article is an attempt to step back from the details and examine the entire issue of real-time problem solving from first principles. We examine the degrees of freedom available in structuring the problem space and the search process to reduce problem-solving variations and produce satisficing solutions within the time available. This structured approach aids in understanding and sorting out the relevance and utility of different real-time problem-solving techniques.

This article summarizes the findings of a 1992 study of knowledge-based systems research and applications in Japan. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.