Free Term Paper on HPKB, the High Performance Knowledge Base
The High Performance Knowledge Bases (HPKB) project demonstrated that the
teams of knowledge engineers working together could create knowledge bases (KBs)
roughly at the rate of 10K axioms/year for a pre-specified task 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 and the knowledge engineer. It was
acknowledged that the ability of a subject matter expert (SME) to directly enter
knowledge is essential to improve the KB construction rates.
The SRI
team is developing a system for direct knowledge entry by SMEs as an integrated
team of technology developers. The SRI team includes Boeing, Information
Sciences Institute (ISI) at University of Southern California, Northwestern
University, Pacific Sierra Research (PSR), Stanford University, University of
Massachusetts at Amherst, University of 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 claim of this effort is that SMEs, unassisted by AI technologists,
can assemble models of mechanisms and processes from components. These models
are both declarative 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 few 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 project 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 principal extension to Cyc and the "HPKB standard" of
knowledge bases, is the idea of declarative 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 part 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.
The development of libraries of components made available to
SMEs via restricted natural language based, graphical, or templatized interfaces
is the principal means by which logic-oriented knowledge representation
formalisms become accessible to ordinary users. Every modeling technology shows
this progression: Spreadsheets, finite-element packages, statistical packages,
chemical synthesis software, Macsyma and Mathematica, architectural and CAD
packages, graphics and HCI systems, etc., are accessible to ordinary users
because they offer libraries of components. As a practical matter, then, it
makes sense to provide SMEs with libraries of modeling components. As a
scientific matter, we believe we can develop components that represent how
humans think about mechanisms and processes.