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RE: First Order Logic and Semantic Web RE: NPR, Godel, Semantic W eb
- From: "Bullard, Claude L (Len)" <firstname.lastname@example.org>
- To: Jeff Lowery <email@example.com>
- Date: Fri, 11 May 2001 13:19:18 -0500
Yes, the problems of amplification and catastrophe
in a feedback system: well, essentially, at onset,
you have to *feel* it and put your palm on the
strings before the speakers blow... ;-) (the answer
is in the feedback formula; the control
or policy for returning output to input).
Let me ask you this, how does a human negotiate for a
used car? In other words, many contracts start out with
only a minimal amount of trust among the partners in
the transaction. Ask yourself in any trading situation
what procedures or tasks do you do to ensure the situation
meets your needs. How do you express those needs to a
I see these as separate issues: logical procedures
for negotiating a basis for trust, maintaining a
private registry of trusted partners, creating a
trustworthy knowledge base. How does the Survivor
game on TV work (never watch it myself - degrading)?
I should think one would look at the UDDI/WSDL service
model and find the place where the ontology fits. What
service is it providing?
As to **how does one train an agent**, I should think that
the critical question. See DAML. What is the agent
allowed to DO? Get to that first.
How do we constrain human agents? Protocol, policy,
backups, reviews, etc. I submit one has to look very
hard at negotiation in contexts of policy and opportunism.
Style counts for humans. For SW? It depends on just how
complex a logical layer you want to devise, the kinds of
agents, how much analogical reasoning you enable, etc.
If you want a thought experiment, the hottest domain for
research at the moment is using an avatar or virtual human
interface as the GUI. What would you need to make that
believable (not real, but believable in the sense that
you know Bugs Bunny is not real, but he is believable)?
Building the knowledge base, as hard as it looks today,
is probably tedious but easier than what follows. After
that, the layer that enables the agent semi-autonomous
capacity to evolve a strategy in moreorless real time
is the hard part. It is a problem similar if not identical
to the problems of interactive fiction and believable
characters (which is why some of us work in that field -
fun, artsy, and illuminating).
So good question: how does one train an agent? Well,
first the agent needs memory, both of specific
facts and what was once called, episodic memory so it
has the capacity to work with stereotypes and match
reactions to events (feel it; put palm on strings). If a stereotype
is identified, how can it avoid falling into local minima?
Annealing was once a topic of discussion in that context.
But before we get that deep, basic WSDL, routing of application
data to application, transforms, etc. Most of the business
documents and business logic are tested long before you
commit a mission critical operation to them. The applications
in those domains are actually unlikely to be as open as the
web. That is the flaw in open vs closed system assumptions.
There is a middle ground (the keiretsu) in which the operational
chain is defined by contract, tested, and known. It is closed
in the sense that expectations are defined and tested prior to
committing resources to it, so it is not chaotically seeking
patterns; it is opportunistic.
Ekam sat.h, Vipraah bahudhaa vadanti.
Daamyata. Datta. Dayadhvam.h
From: Jeff Lowery [mailto:firstname.lastname@example.org]
How does one train an agent?