On Monday, 15 July 1991, prior to the Ninth National Conference on Artificial Intelligence (AAAI-91) in Anaheim, California, over 50 scientists and engineers attended the AAAI-91 Workshop on Intelligent Multimedia Interfaces. The purpose of the workshop was threefold: (1) bring together researchers and practitioners to report on current advances in intelligent multimedia interface systems and their underlying theories, (2) foster scientific interchange among these individuals, and (3) evaluate current efforts and make recommendations for future investigations.

The Executive Council may change the principal office in California The name of this corporation shall be the American Association from one location to another. The corporation may have such other offices, either within or without the State of California, ARTICLE II. This corporation is a nonprofit public benefit corporation and is not organized for the private gain of any person. MEMBERS is organized under the California Nonprofit Corporation Law for scientific and educational purposes in the field of Section 1. Classes and Privileges. Student members have all the rights and privileges of Regular ARTICLE III. The Executive Council shall determine (a) This corporation is organized and operated exclusively the qualifications for membership in the corporation.

Human error is an Criticism should not be querulous, and umes of fast-changing increasingly important wasting, all knife and root puller, but guiding, sensory data that and addressable instructive, inspiring, a South wind, one needs to process concern in modernday not an East wind. Most institutions), and the automation that technology represents accidents waiting to surrounds us (for example, unfriendly computers happen. For example, in the Challenger explosion, We get by because humans excel at coping. the shortcomings of the O-rings had been High-technology accidents occur because known for several years. What feedback hundreds of alarms simultaneously all contributed strategy (for example, story telling, first-principle to the disaster. Likewise, when the lecturing) will most constructively correct British fleet was sent to defend the Falkland the human error? It was at this differences. However, there are no point that the Argentines released their missile models there or in the AI literature of errors and sank an unsuspecting British ship. The operator had The errors result from proficient task performers no inkling of the ramifications of the system practicing in a natural environment; they designs under the current operating conditions. New error and critiquing models operator has virtually no way out. The remarkable need to capture and reflect this difference. We computer-aided design (ICAD) to mitigate begin by examining the design process and such problems. Specifically, we examine the the cognitive difficulties it poses. The designer uses a interference problems are also increasingly variety of cognitive operators to generate a evident on civilian automobiles, airplanes, design, test it under various conditions, refine and ships that cram telephones, radios, computers, it until a stopping rule is reached, and then radar devices, and other electromagnetically store the design as a prototype or analog to incompatible devices into close help start a new process for the next design proximity. The design process is sufficiently complex domain are relevant to all engineering design that a correct and complete design applications that must factor any operational simply cannot be deduced from starting conditions (or manufacturability, sales, or other downstream) or simulation model results.

The Fredkin Challenge in AI Research and Applications at Chandrasekaran, B. see nlso Hendler, Match, 2(2): Summer 1981, 30-33.

My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. It was motivated by two difficulties in scaling up existing generators. Current generators only accept input that are relatively poor in information, such as feature structures or lists of propositions; they are unable to deal with input rich in information, as one might expect from, for example, an expert system with a complete model of its domain or a natural language understander with good inference ability. Current generators also have a very restricted knowledge of language -- indeed, they succeed largely because they have few syntactic or lexical options available (McDonald 1987) -- and they are unable to cope with more knowledge because they deal with interactions among the various possible choices only as special cases. To address these and other issues, I built a system called FIG (flexible incremental generator). FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Computation is done by spreading activation across the network, supplemented with a small amount of symbolic processing. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al. 1988).

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems.

Technical Report RL-TR-92-217, Rome Laboratory, Air Force Systems Command , Griffiss Air Force Base, NY 13441 -5700

Sherif M. Botros Christopher G. Atkeson Brain and Cognitive Sciences Department and the Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dynamics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance.

The development of learning algorithms is generally based upon the minimization of an energy function. It is a fundamental requirement to compute the gradient of this energy function with respect to the various parameters of the neural architecture, e.g., synaptic weights, neural gain,etc. In principle, this requires solving a system of nonlinear equations for each parameter of the model, which is computationally very expensive. A new methodology for neural learning of time-dependent nonlinear mappings is presented. It exploits the concept of adjoint operators to enable a fast global computation of the network's response to perturbations in all the systems parameters. The importance of the time boundary conditions of the adjoint functions is discussed. An algorithm is presented in which the adjoint sensitivity equations are solved simultaneously (Le., forward in time) along with the nonlinear dynamics of the neural networks. This methodology makes real-time applications and hardware implementation of temporal learning feasible.

In this paper we show that discrete affine wavelet transforms can provide a tool for the analysis and synthesis of standard feedforward neural networks. Itis shown that wavelet frames for L2(IR) can be constructed based upon sigmoids. The spatia-spectral localization property of wavelets can be exploited in defining the topology and determining the weights of a feedforward network. Training a network constructed using the synthesis proceduredescribed here involves minimization of a convex cost functional andtherefore avoids pitfalls inherent in standard backpropagation algorithms. Extension of these methods to L2(IRN) is also discussed. 1 INTRODUCTION Feedforward type neural network models constructed from empirical data have been found to display significant predictive power [6]. Mathematical justification in support ofsuch predictive power may be drawn from various density and approximation theorems [1, 2, 5].