Previously, we had developed the concept of a Segmental Neural Net (SNN) for phonetic modeling in continuous speech recognition (CSR). This kind of neu ral network technology advanced the state-of-the-art of large-vocabulary CSR, which employs Hidden Marlcov Models (HMM), for the ARPA 1oo0-word Resource Management corpus. More Recently, we started porting the neural net system to a larger, more challenging corpus - the ARPA 20,Ooo-word Wall Street Journal (WSJ) corpus. During the porting, we explored the following research directions to refine the system: i) training context-dependent models with a regularization method; ii) training SNN with projection pursuit; and ii) combining different models into a hybrid system. When tested on both a development set and an independent test set, the resulting neural net system alone yielded a perfonnance at the level of the HMM system, and the hybrid SNN/HMM system achieved a consistent 10-15% word error reduction over the HMM system. This paper describes our hybrid system, with emphasis on the optimization methods employed.

We study feed-forward nets with arbitrarily many layers, using the standard sigmoid, tanh x. Aside from technicalities, our theorems are: 1. Complete knowledge of the output of a neural net for arbitrary inputs uniquely specifies the architecture, weights and thresholds; and 2. There are only finitely many critical points on the error surface for a generic training problem. Neural nets were originally introduced as highly simplified models of the nervous system. Today they are widely used in technology and studied theoretically by scientists from several disciplines. However, they remain little understood.

Fall 1994, 63-75 Abbott, Kathy, see Orlando, Nancy AI and NP-Hard Problems: 1993 Spring Alterman, Richard, see Hendler, James Abhyankar, R. B. Review of Computing Symposium Report.

MT Telecom, a Dutch telecommunications utility, has installed expert BNR Europe (Harlow, England), the instrument aboard the satellite. Pending system-based help desk systems to R&D subsidiary of telecommunications NASA approval, EUVE will be the centralize its 23 networked local data equipment supplier Northern first orbiting astrophysics mission to Telecom, is using virtual reality technology replace humans with AI technology. This installation proved to be a critical planning. The VR system allows Re:Member Data Services (Memphis, factor in helping the company BNR's engineers to visualize complex Tenn.), a data processor for obtain the IS0 9000 Total Quality installations and how they will work, credit union software services, has System Standard certification, a greatly saving time and effort compared automated all company service and requirement for those organizations to the traditional CAD system. Continental Bank (Chicago, Ill.) has expert system tracks all requests developed a client/server-based intelligent called in by users, and all requests Lockheed Missiles ST Space (Palo application to improve the can be accessed by anyone at the Alto, Calif.) has developed ASAP quality of its customer service.

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers.

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.

This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers. The final report has benefited greatly from their comments and from textual revisions contributed by Joseph Halpern, Fernando Pereira, and Dana Nau.

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.