With solvers you often have this information, you have the constraint
model - the rules themselves tell you the possible ranges and values.
Mark
Michael Neale wrote:
with more "hints" from the fact model (or an ontology) we could do
more. In Pojo land this could be done with annotations, with OWL its
more built in etc etc...
or course training data can be more optimal (as relying on meta data
to tell us assumes that the user knows what they are talking about !).
Training or runtime data collectino is ultimately the way to go I think.
On 12/15/06, *Mark Proctor* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
Actually I was just thinking about some stuff Edson has done. With
solvers we know the available data and ranges, right? We can use
this to
order indexes, I know this was something Edson looked into - but
without training data, we couldn't make it worth while - same for
custom indexing. So we can start to incorporate those to get faster
joins for known data sets.
Mark
Geoffrey De Smet wrote:
> The more I learn from JCHS (or prolog for that matter),
> the more I am starting to think that this is a different way of
solving.
>
> 1) JCHS/prolog looks like (or is) declarative solving.
>
> 2) Taseree is actually more hybrid, the general idea behind it is:
> - Drools (declarative programming) is very easy for evaluation
> but very difficult for solving.
> - Local/tabu search (procedural programming) is easy for solving
> but difficult for evaluation.
>
>
> Both have it's disadvantages and advantages, for example:
> Local search is generally faster but doesn't recognize the optimal
> solution.
>
> To me it seems they are both interesting to implement,
> there must be some common ground too.
> We should hold a conference call about it this weekend?
>
> It would be a good idea to compare JCHS and Taseree on a couple of
> problems, like the tt problem:
> http://mat.gsia.cmu.edu/TOURN/
>
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