Steven Rehfuss

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Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven’t, the task can often prove impossible. One reason is that the requirementsoriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge(More)
Knowledge engineering suffers from a lack of formal tools for understanding domains of interest. Current practice relies on an intuitive, informal approach for collecting expert knowledge and formulating it into a representation scheme adequate for symbolic processing. Implicit in this process, the knowledge engineer formulates a model of the domain, and(More)
This paper discusses the current state of the art of industrial neurocomputing, and then speculates on its future. Three examples of commercial neuro-silicon are presented: the Adaptive Solutions CNAPS system, the Intel ETANN chip, and the Synaptics OCR chip. We then speculate on where commercial neurocomputing hardware is going. In particular we propose(More)
In systems that process sensory data there is frequently a model matching stage where class hypotheses are combined to recognize a complex entity. We introduce a new model of parallelism, the Single Function Multiple Data (SFMD) model, appropriate to this stage. SFMD functionality can be added with small hardware expense to certain existing SIMD(More)
INKA is a natural language interface to facilitate knowledge acquisition during expert system development for electronic instrument trouble-thooting. The expert system design methodology develops a domain definition, called GLIB, in the form of a semantic grammar. This grammar format enables GLIB to be used with the INGLISH interface, which constrains users(More)
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