Failing to recognize this, significance. All interested readers The medium has misplaced the message understanding intelligence and cognition should be directed to his Ph.D. thesis [that should have appeared in Dr. Franck can be reached is merely irreverent, not irrelevant, per se " As readers can see, quite at the following address Dr. Bruno to AI All I can say is mea Thank you Columbus, OH 43210 culpa, and I hope this letter may help James R Slagle and Michael R. Wick to square things I read with great interest the excellent Information Processing. Engineering" by Ken Forbus in AI manuscript, I somehow managed to Professor Forbus's forceful changed the intended meaning rather Our recent article, entitled "A I personally as published reads: "The goal of these examples of our evaluation process thank him for writing such an eloquent gatherings has been to understand One of these examples involved the Various groups, especially for narrowly defined tasks (expert systems)." Naturally, I would not expect Prof. Forbus to enumerate IIICAD stands for "Intelligent, Integrated, and Interactive CAD" and was CWI is a research AAAI Membership Directory center in pure and applied mathematics An invaluable networking tool, this annual roster of AAAI members and computer science at Amsterdam. You've heard of this AAAI conference-it's the most distinguished Email: paulh@cwi.nl. TR An exciting opportunity to view the latest Al products, services, and CSR8744, CWI, Amsterdam research from industry and the academic community. These are the state-of-the-art research papers presented at the AAAl's A copy of this publication is included in conference Data Description Language for Coding registration; AAAI members not attending the conference may purchase Design Knowledge."

It 445 Burgess Drive In spite of the many strong technical was generally agreed that the formalization Menlo Park, CA 94025-3496 results that have been produced, it is of commonsense reasoning (415) 328-3123 still far from clear whether existing should be a top-level item for future approaches are sufficient to formalize research.

Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell--for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a. genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components—nodes and arcs—as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an example of a real world application, MUN1N an expert system for electromyography is discussed.IJCAI-89, Vol. 2, pp. 1080–1085

We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robotsJournal of The British Interplanetary Society, Vol. 42, pp 478-485

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ABSTRACT A novel network type is introduced which uses unit-length 2-vectors for local variables. As an example of its applications, associative memory nets are defined and their performance analyzed. Real systems corresponding to such'phasor' models can be e.g. INTRODUCTION Most neural network models use either binary local variables or scalars combined with sigmoidal nonlinearities. Rather awkward coding schemes have to be invoked if one wants to maintain linear relations between the local signals being processed in e.g.

This arises from representation which gives rise to gentle degradation as faults appear. These functions are attractive to implementation in VLSI and WSI. For example, the natural be useful in silicon wafers with imperfect yield, where thefault - tolerance could is approximately proportional to the non-functioning siliconnetwork degradation area. To cast neural networks in engineering language, a neuron is a state machine that is either "on" or "off', which in general assumes intermediate states as it switches The synapses weighting the signals from asmoothly between these extrema.

An increasing number of profoundly deaf patients suffering from sensorineural deafness are using cochlear implants as prostheses. Mter the implant, sound can be detected through the electrical stimulation of the remaining peripheral auditory nervous system. Although great progress has been achieved in this area, no useful speech recognition has been attained with either single or multiple channel cochlear implants. Coding evidence suggests that it is necessary for any implant which would effectively couple with the natural speech perception system to simulate the temporal dispersion and other phenomena found in the natural receptors, and currently not implemented in any cochlear implants. To this end, it is presented here a computational model using artificial neural networks (ANN) to incorporate the natural phenomena in the artificial cochlear.

An increasing number of profoundly deaf patients suffering from sensorineural deafness are using cochlear implants as prostheses. Mter the implant, sound can be detected through the electrical stimulation of the remaining peripheral auditory nervous system. Although great progress has been achieved in this area, no useful speech recognition has been attained with either single or multiple channel cochlear implants. Coding evidence suggests that it is necessary for any implant which would effectively couple with the natural speech perception system to simulate the temporal dispersion and other phenomena found in the natural receptors, and currently not implemented in any cochlear implants. To this end, it is presented here a computational model using artificial neural networks (ANN) to incorporate the natural phenomena in the artificial cochlear.

W. Scott Stornetta Stanford University, Physics Department, Stanford, Ca., 94305 Tad Hogg and B. A. Huberman Xerox Palo Alto Research Center, Palo Alto, Ca. 94304 ABSTRACT Recognizing patterns with temporal context is important for such tasks as speech recognition, motion detection and signature verification. We propose an architecture in which time serves as its own representation, and temporal context is encoded in the state of the nodes. We contrast this with the approach of replicating portions of the architecture to represent time. As one example of these ideas, we demonstrate an architecture with capacitive inputs serving as temporal feature detectors in an otherwise standard back propagation model. Experiments involving motion detection and word discrimination serve to illustrate novel features of the system.