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   RE: [xml-dev] Beyond Ontologies

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I agree about the expense, but in studying the web as a 
system, it is useful to forgo the technical web architecture 
and come to understand that its complexity emerges from 
the interaction of agents both human and automated, and 
for the semantic web future, agents empowered to act 
on behalf of human owners, thus The Golem Problem I 
discussed here and in the Markup Languages MIT magazine 

Asking isn't the whole solution, but even in agent-based 
modeling, it is an important task.  For many 
kinds of conflicts (say the human profile), it is 
the only appropriate place to start.  The toughest 
problem of agent-based modeling is not knowing 
what the human owner wants, and worse, that the 
human does not know what they want.  They act 
on habit formation which might be called 'instinct'. 
Understanding habit formation with regards to 
symbol/sign assignment is important to finding 
the conflict of semantics.  The interface aspects 
are VERY important and why marketing designers 
and for their own good, web GUI designers, should 
study semiotics.  And so should we because semiotics 
are important to how and why web systems become complex.

Here are some notes from the article you reference for
others who are only lightly following this thread, to 
illuminate the topics, edited for brevity so not 
exactly quoted.  Comments in brackets are mine.  I start 
with the Eurobios site definitions:

Also from Eurobios site:

Complex Systems:  elaborate and unpredictable properties arise from
agents.  Examples of emergent properties include how the system organizes
how it finds balance of order and disorder, how agents individually and
evolve new behaviors in response to change.

Emergent behavior:  emerges from interactions with other agents and
rather than being imposed because behavior cannot be deduced from the rules.

Co-adaptation:  actions of all agents in environment affect each other, such

that they co-adapt and co-evolve.  Competitive advantage is gained by
adapting to novel and unpredictable situations faster than the competition
the setup).

Then the Economist article:

"Complex Agent-Based Dynamic Network: explain behaviour through the use of
a program that acts in a self-interested manner in its dealings with
numerous other 
agents inside a computer. Can mimic almost any interactive system. If the
parts can be understood, some insight into the whole will follow.  

[The goal is prediction.  Simulation or role play outs the intent of the 
communication:  what the agent wants.  Hint:  what are the public and 
private couplers?]

One of the challenges in setting up an agent-based model is defining clearly

what individual agents want.

Not only are people often inaccurate in their beliefs about themselves, 
but especially in the business world they lie.

[Measure]: she set out to measure what was actually going on. Spent hours
watching and recording which 
machines were running when.   [See Fisher Information]

[Entropy as measure of system disorganization]

Compared her findings about what was really happening with what people
was happening on paperwork such as invoices. By checking how often these
she could approximate the mathematical entropy of the system: a measure of
how disordered 
it was. 

[Entropy as measure of real time knowledge.  Compare Boltzman and Shannon 
entropies and the concept of addressing.  Why URIs and the problem of 
URI management]

A complicated factory, with many different assembly lines, can still be
if everything is proceeding according to plan. Even a simple one, by
would be disordered if managers had no idea of what was happening."


From: Didier PH Martin [mailto:martind@netfolder.com]

Practically, since we tried that in the past, asking to humans to resolve
issues is long and costly process. Moreover, I may have some difficulties to
ask the others to resolve all ontological conflicts I will encounter on the
web. However, I think, intuitively, you point to a good direction. More and
more I think that agents based modeling can help a lot for this type of
phenomenon hard to model the usual way.

For those of us who want to explore the agents based modeling here is a good
link. http://www.econ.iastate.edu/tesfatsi/ace.htm. It's about using agents
based modeling in the context of economics but it could be very well applied
on the topic of concept categorization or concept clustering. The site links
to a lot of articles with a pedagogical perspective. 

I think that agents based modeling can be tremendously useful for a
_pratical_ semantic web.


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