We present results on the use of neural network based autoassociators which act as novelty or anomaly detectors to detect imminent motor failures. The autoassociator is trained to reconstruct spectra obtained from the healthy motor. In laboratory tests, we have demonstrated that the trained autoassociator has a small reconstruction error on measurements recorded from healthy motors but a larger error on those recorded from a motor with a fault. We have designed and built a motor monitoring system using an autoassociator for anomaly detection and are in the process of testing the system at three industrial and commercial sites.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

The value of scientific digital-image libraries seldom lies in the pixels of images. For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. SKICAT integrates techniques for image processing, classification learning, database management, and visualization. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent. This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date. Hence, learning algorithms played a powerful and enabling role and solved a difficult, scientifically significant problem, enabling the consistent, accurate classification and the ease of access and analysis of an otherwise unfathomable data set.

The goal of this workshop was which was held in June 1995 in San istributed artificial intelligence the cooperative solution of "making connections," trying to better Francisco. The DAI Workshop problems in multiagent intelligent understand the connections received financial support from the systems with both computational between DAI and related fields (for American Association for Artificial and human agents. The central problem example, computer-supported cooperative Intelligence as well as the Boeing in DAI is how to achieve coordinated work, group decision support Company. Registration materials for the Thirteenth National Conference on Artificial Intelligence (AAAI-96), the Eighth Innovative Applications of Artificial Intelligence Conference (IAAI-96), and the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) are now available from the AAAI office at ncai@aaai.org Copies of the AAAI-96 registration brochure are being mailed to all AAAI members.

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Randy Davis announced the appointment of six new program managers at ARPA. At IJCAI-95, Randall Davis assumed the office of president of the American For many attending the Fourteenth for consideration this year," noted Association for Artificial Intelligence International Joint Conference on Ray Perrault of SRI International, (AAAI). Davis is a professor of Artificial Intelligence (IJCAI-95), the chair of the conference. "This is more electrical engineering and computer most difficult problem was choosing than at IJCAI-93 and at recent science and associate director of the which session to attend in the rich, National Conferences on AI in the AI Lab at the Massachusetts Institute varied program. Davis succeeds data-mining application from rate was under 25 percent, showing Barbara Grosz, Gordon McKay professor the U.S. Department of the Treasury that there is a great deal of work of computer science in the Division that identifies potential money laundering going on, and the scientific standard of Applied Sciences at Harvard to a small mobile LEG0 robot of IJCAI matches or exceeds that of University.

This report stems from a workshop that was organized by the Association for the Advancement of Artificial Intelligence (AAAI) and cosponsored by the Information Technology and Organizations Program of the National Science Foundation. The purpose of the workshop was twofold: first, to increase awareness among the artificial intelligence (AI) community of opportunities presented by the National Information Infrastructure (NII) activities, in particular, the Information Infrastructure and Tech-nology Applications (IITA) component of the High Performance Computing and Communications Program; and second, to identify key contributions of research in AI to the NII and IITA.

Padhraic Smyth Jet Propulsion Laboratory 238-420 California Institute of Technology 4800 Oak Grove Drive Pasadena, CA 91109 Abstract This paper describes probabilistic methods for novelty detection when using pattern recognition methods for fault monitoring of dynamic systems. The problem of novelty detection is particularly acutewhen prior knowledge and training data only allow one to construct an incomplete classification model. Allowance must be made in model design so that the classifier will be robust to data generated by classes not included in the training phase. For diagnosis applications one practical approach is to construct both an input density model and a discriminative class model. Using Bayes' rule and prior estimates of the relative likelihood of data of known and unknown origin the resulting classification equations are straightforward.