The distance measure and learning problem are formally described as nested objective functions. We derive an efficient algorithm by using optimization techniques that allow us to divide up the objective function into parts which may be minimized in distinct phases. The algorithm has accurately recreated 10 prototypes from a randomly generated sample database of 100 images consisting of 20 points each in 120 experiments. Finally, by incorporating permutation invariance in our distance measure, we have a technique that we may be able to apply to the clustering of graphs. Our goal is to develop measures which will enable the learning of objects with shape or structure. Acknowledgements This work has been supported by AFOSR grant F49620-92-J-0465 and ONR/DARPA grant N00014-92-J-4048.

This paper describes the use of a convolutional neural network to perform address block location on machine-printed mail pieces. Locating the address block is a difficult object recognition problem because there is often a large amount of extraneous printing on a mail piece and because address blocks vary dramatically in size and shape. We used a convolutional locator network with four outputs, each trained to find a different corner of the address block. A simple set of rules was used to generate ABL candidates from the network output. The system performs very well: when allowed five guesses, the network will tightly bound the address delivery information in 98.2% of the cases. 1 INTRODUCTION The U.S. Postal Service delivers about 350 million mail pieces a day.

Critical features of a domain (such as invariance under translation, rotation, and permu- Clustering with a Domain-Specific Distance Measure 103 tation) are captured within the clustering procedure, rather than reflected in the properties of feature sets created prior to clustering. The distance measure and learning problem are formally described as nested objective functions. We derive an efficient algorithm by using optimization techniques that allow us to divide up the objective function into parts which may be minimized in distinct phases. The algorithm has accurately recreated 10 prototypes from a randomly generated sample database of 100 images consisting of 20 points each in 120 experiments. Finally, by incorporating permutation invariance in our distance measure, we have a technique that we may be able to apply to the clustering of graphs. Our goal is to develop measures which will enable the learning of objects with shape or structure. Acknowledgements This work has been supported by AFOSR grant F49620-92-J-0465 and ONR/DARPA grant N00014-92-J-4048.

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. 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.