RE: First Order Logic and Semantic Web RE: NPR, Godel, Semantic W eb

["Bullard, Claude L (Len)" <clbullar@ingr.com>]

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 
potential partner?

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.

Len 
http://www.mp3.com/LenBullard

Ekam sat.h, Vipraah bahudhaa vadanti.
Daamyata. Datta. Dayadhvam.h


-----Original Message-----
From: Jeff Lowery [mailto:jlowery@scenicsoft.com]

How does one train an agent?