THE MODEL; STOCHASTIC RESONANCE The reduced neuron model consists of a single Hopfield-type computational element, which may be modeled as a R-C circuit with nonlinear feedback provided by an operational amplifier having a sigmoid transfer function.

Because eye muscles never cocontract and do not deal with external loads, one can write an equation that relates motoneuron firing rate to eye position and velocity - a very uncommon situation in the CNS. The semicircular canals transduce head velocity in a linear manner by using a high background discharge rate, imparting linearity to the premotor circuits that generate eye movements. This has allowed deducing some of the signal processing involved, including a neural network that integrates. These ideas are often summarized by block diagrams. Unfortunately, they are of little value in describing the behavior of single neurons - a fmding supported by neural network models.

A neurophysiologically-based model is presented that controls a simulated kinematic arm during goal-directed reaches. The network generates a quasi-feedforward motor command that is learned using training signals generated by corrective movements. For each target, the network selects and sets the output of a subset of pattern generators. During the movement, feedbackfrom proprioceptors turns off the pattern generators. The task facing individual pattern generators is to recognize when the arm reaches the target and to turn off. A distributed representation of the motor commandthat resembles population vectors seen in vivo was produced naturally by these simulations.

We propose and empirically evaluate a method for the extraction of expertcomprehensible rules from trained neural networks.

The Digital Services Research Group and its predecessor groups and offshoots in Digital Equipment Corporation have been mobilizing leading-edge AI research to bear on real-life problems that face the corporation and its customers. The general strategy of the group is to explore emerging techniques relevant to service and support needs through developing rapid prototypes, deploying these prototypes, and incorporating feedback from users. With over 32 major projects undertaken during the past decade, we have worked on broad spectrum of problems and explored a variety of advanced AI techniques. This article describes the current AI activities in five areas: (1) enterprise advisory systems, (2) natural language processing and textual information retrieval, (3) largescale knowledge base management and access, (4) software configuration management, and (5) intrusion detection.

This article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.

Books in the Innovative Applications of Artificial for the documents they want.

Allen Newell was one of the founders and truly great scientists of AI. His contributions included foundational concepts and ground-breaking systems. His career was defined by the pursuit of a single, fundamental issue: the nature of the human mind. This article traces his pursuit from his early work on search and list processing in systems such as the LOGIC THEORIST and the GENERAL PROBLEM SOLVER; through his work on problem spaces, human problem solving, and production systems; through his final work on unified theories of cognition and SOAR.

This technology was developed with funding from the National Sony, the Japanese consumer electronics Science Foundation. Working with experts from Armco Steel (Middletown, company, has developed OH), Carnegie Group developed a prototype system to diagnose an intelligent system to improve chatter in a coldrolling mill. In the and consulting company, has developed a PCbased virtual reality system company's semiconductor group, to provide financial planners a visual metaphor for viewing large The system allows the user to "fly" over the The expert system is installed in Meiji's Tokyo service two-thirds. With Domain Dynamics Ltd. (Windsor, England) has developed a PCbased the system, technical support neural network application to automate the recognition of data from the Currently available in days to solve with a text retrieval the form of two circuit boards, TESPAR (which stands for Time Encoded system now take just afew minutes. Signal Processing and Recognition) is capable of being converted to a single piece of silicon.

De Kleer agreed Institute for the Learning Sciences, university laboratories to real-world that model construction can be difficult Troy Heindel of the Gensym Corporation development efforts, state-of-the-art but noted that the overhead involved (formerly of NASA Johnson research in model-based reasoning in developing structural, Space Center [JSC]), Ben Kuipers of (MBR), and an overview of relevant functional, or causal models is not the University of Texas at Austin, research and applications activities in worse than that associated with developing Ethan Scarl of Boeing Computing the European Space Agency (ESA).