Nestor Inc. (Providence, R.I.) and Intel Corp. (Santa Clara, Cal.) have The US Army Research Lab and the Knowledge Engineering Group of the US delivered the first samples of a Army Ordnance Center and School (Aberdeen Proving Grounds, Md.) have jointly developed, second-generation developed a visual expert system for diagnostics of the Ml tank's turbine engine. A visualization of the East Quayside area, including landscaping, American Medical Laboratories the road network, buildings, and the Tyne Bridge landmark, is being created (Chantilly, Va.) has implemented as a virtual world. Prospective tenants and purchasers will be able to three speech recognition systems to experience a "walk through" of the buildings. Togai InfraLogic (Irvine, Cal.) has been awarded a Phase II Small Business Innovation The three VoicePath systems, developed Research (SBIR) grant by NASA Johnson Space Center to study fuzzy by Kurzweil AI (Waltham, logic control for improving performance of thermal control systems, including Mass.), contain a 50,000-word dictionary, industrial applications such as air conditioning and energy control. Their research is aimed at helping manufacturers Sciaky (Chicago, Ill.), a developer improve their products while trimming production and retooling costs.

It is Lynn Andrea Stein, Carol AAAI's new Secretary/Treasurer.

This article contains an overview of recent and ongoing projects at Boeing's Huntsville Advanced Computing Group (ACG). In addition, it contains an overview of some of the work being conducted by Boeing's Advanced Civil Space Systems Group. One aspect of ACG's charter is to support the efforts of other groups at Boeing. Thus, AI is not considered a stand-alone field but, instead, is considered an area that can be used to find both long- and short-term solutions for Boeing and its customers. All the projects listed here represent a team effort on the part of both ACG researchers and members of other Boeing organizations.

The first Robotics Exhibition and Competition sponsored by the Association for the Advancement of Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future.

Computational Linguistics, 19 (2), 313-30.

In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute.

Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI93), American Association for Artificial Intelligence, Washington, DC, July 1993, pp. 392-397.

We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation. 1 Background: The Deep Space Network The Deep Space Network (DSN) (designed and operated by the Jet Propulsion Laboratory (JPL) for the National Aeronautics and Space Administration (NASA)) is unique in terms of providing end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system. The ground component of the DSN consists of three ground station complexes located in California, Spain and Australia, giving full 24-hour coverage for deep space communications.

An important issue in neural computation is the dynamic range of weights in the neural networks. Many experimental results on learning indicate that the weights in the networks can grow prohibitively large with the size of the inputs. Here we address this issue by studying the tradeoffs between the depth and the size of weights in polynomial-size networks of linear threshold elements (LTEs). We show that there is an efficient way of simulating a network of LTEs with large weights by a network of LTEs with small weights. To prove these results, we use tools from harmonic analysis of Boolean functions.

Padhraic Smyth, J eft" Mellstrom Jet Propulsion Laboratory 238-420 California Institute of Technology Pasadena, CA 91109 Abstract We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation. 1 Background: The Deep Space Network The Deep Space Network (DSN) (designed and operated by the Jet Propulsion Laboratory (JPL)for the National Aeronautics and Space Administration (NASA)) is unique in terms of ...