A Knowledge Entry System for Subject Matter Experts :: essays research papers

The High cognitive operation Knowledge Bases (HPKB) cipher demonstrated that the teams of intimacy engineers wagering together could bring out knowledge bases (KBs) roughly at the rate of 10K axioms/year for a pre-specified line of work and evaluation criteria. The HPKB effort showed that it is possible to create KBs by reusing the content of knowledge libraries, and it demonstrated reuse rates ranging from 25% to 100%, depending on the application program and the knowledge engineer. It was acknowledged that the ability of a subject matter intellectual (SME) to directly enter knowledge is essential to improve the KB bend rates. The SRI team is developing a system for direct knowledge meekness by SMEs as an integrated team of technology developers. The SRI team includes Boeing, learning Sciences Institute (ISI) at University of Southern California, Northwestern University, Pacific Sierra look (PSR), Stanford University, University of Massachusetts at Amherst, University o f Texas at Austin, and University of West Florida. Knowledge Systems Laboratory at Stanford, Pragati Systems, and Massachusetts Insititute of Technology joined the team after the contract award. The yell of this effort is that SMEs, unassisted by AI technologists, can assemble models of mechanisms and processes from components. These models argon both common mood and executable, so questions about the mechanisms and processes can be answered by conventional inference methods (for example, theorem proving and taxonomic inference) and by various task-specific methods (for example, simulation, analogical reasoning, and problem-solving methods). A related claim is that relatively some components, perhaps a few thousand, are sufficient for SMEs to assemble models of virtually any mechanism or process. We claim that these components are independent of domain, and that assembly from components instantiated to a domain is a natural way for SMEs to create KB content. The research in this p roject exploits and extends previous work in the HPKB project, as well as work in process description languages, qualitative physics, systems dynamics, and simulation. One scientific innovation, and the track extension to Cyc and the "HPKB standard" of knowledge bases, is the idea of suggestive and executable models (DEMs) assembled from components. The declarative aspect of DEMs supports conventional inference, whereas the executable aspect supports reasoning by simulation. For example, the declarative break off of a model of aerosols is sufficient to answer questions like, "Will a 5-micron filter afford protection against this aerosol?" while the executable part is necessary to model the dispersal pattern of the aerosol.