>It would be helpful to see a mock up and/or scenario of how this capability
>would be used to perform daily work, like write some code, conduct a
>meeting, make a phone call, read a book, design a computer chip, fix the
>car, go to the dentist, the normal activity people use "intelligence" to
>support in generating knowledge about the world that might be useful in a
>repository.
>There has been consideration for the DKR team to create a tool to help
>software engineering. The things you describe today seem like they might
>be useful for that task, but also help other tasks, as well. Does this
>suggest that augmenting "intelligence", which you mention elsewhere,
>provides an underlying capability that can help everyone do almost anything
>a little better?
>Rod.
The DKR team has identified two distinct levels of information abstraction
that require development to achieve the group's goals. The underlying
abstraction appears to be based upon a general system cognitive model based
upon deterministic behavior that can machine execute. This technology model
could be used as a foundation to design and implement "An interactive tool
for discussion and *deliberation* that records decisions and their
rationales in a way that allows the knowledge gained in the process to be
applied to future projects."
To fully achieve the interactive tool goal, the fundamental capabilities in
the underlying technology model must include a robust way to traverse, edit,
read, and write untyped text. In addition, there needs to be a way to
intelligently analyze the text in interesting ways to determine fundamental
semantics in the symbolic patterns and link these patterns to other passages
to anywhere in the web. This text may be linked to typed atomic data that
may itself be composed into typed molecular data representing pictures,
sounds, and other rich multimedia information. All this information may
itself exist as part of a data structure within a domain object within a
system.
The SGML community has a long history of developing ways to markup documents
to capture semantic knowledge embedded in strings. As processing
requirements become more sophisticated, new ways of managing this complexity
need to be developed. One possible solution is to move to a clear document
model. This model separates concerns by parsing the clear text from the
markup information. The clear text is parsed into a collection of linked
character nodes, while one or more composite structure processors maintain
position and range links into the clear text collection. Each processor may
have specialized behavior to analyze and hold semantic information on
format, organization, navigation, narrative, reference, graphic control,
publication, and filters. The model must be able to allow clear text editing
while automatically maintaining the processor links into the clear text
collection. Such a model would be able to manage the requirements for a
robust DKR environment.
Sandy Klausner, CTO
Cubicon Technologies Corporation
klausner@cubicon.com
(408) 867-1100
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