SUO: Re: Architecture of an intelligent ontology development algorithm
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
Rich,
I would have thought that a fairer summary of what's been said here
all summer long on many different threads is that there is really no
such thing as a hypothesis-free algorithm for discovery -- actually,
that's more like a summary of what's been discovered about discovery
over the last few thousand summers, but who's counting? So I think
that a better question might be something along the following lines:
How are concept-driven (analytic, axiomatic, rationalist, top-down) methods
and data-driven (synthetic, contingent, empiricist, bottom-up) procedures
best to be integrated in human inquiry, or in the reconstitutions thereof,
given that the distinction between analytic and synthetic is more relational,
interpretive, or "situated" than it is absolute, invariant, or "essential"?
A start on answering that question might be to get a better analysis of the
similarities among and the differences between the various types of reasoning
that need to be integrated. On that score, my advice would be: Read more Peirce.
Jon Awbrey
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
Richard Cooper wrote:
>
> Since many of us seem to agree that a bottom-up
> algorithm could be used to produce the axiom
> set of an ontology through situated experience
> in the real world, I'm trying to draft some
> requirements for this algorithm.
>
> There is a very suggestive paper at
> http://jasss.soc.surrey.ac.uk/6/3/1.html
> "Discrete Agent Simulations of the Effect
> of Simple Social Structures on the Benefits
> of Resource Sharing".
>
> The paper desribes a simulation of agents in an
> environment somewhat like early human societies
> are thought to have evolved in.
>
> A similar approach could be used to measure the success of
> each strategy on the basis of how successful agents use that
> strategy. In a simulated environment, instead of a situated
> one, its easy to measure behaviors and organize them according
> to what works well and what doesn't.
>
> So in a situated environment, perhaps the algorithm can guess at
> axioms based on fragments of previous guesses that were successful.
> The so-called evolutionary algorithms could suggest requirements for
> monitoring the algorithm's behavior in the real world, measuring success
> and failure, and buliding a database of experience for process improvement.
>
> So it seems to me that the process improvement concepts should be
> a top level ontology in an algorithm that learns still higher level
> axioms, while the WordNet concept set provides at least the words for
> communicating with real world people.
>
> Any thoughts on this subject?
>
> Thanks,
> Rich
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o