Short explanation first:
STI: This value indicates how relevant this atom is to the currently 
running process/context
LTI: This value indicates how relevant this atom might be in future 
processes/context (Atoms with low LTI have no future use and get delete if 
the AS gets to big)
VLTI: This is a simple boolean that indicates that this atom should never 
be deleted. (Useful for system components that are written in Atomese)

so STI values are only ever useful at the current point in time so storing 
them in a DB makes no sense. Now storing only those Atom which have a high 
STI/LTI in the DB that might be useful but there is no code that does that 
currently. In my opinion just storing the whole AS in a DB works just as 

Now when you load the atoms back into the AS then it might make sense to 
give all those Atoms an LTI boost.

I am not sure what exactly you want to use the AttentionValues for but in 
general they are supposed to speed up other processes like the PLN System 
by restricting their search space to only Atoms with a high STI.

Misgana has a working but experimental implementation for this as far as I 

In regards to using ECAN:
generate fake sentences --> feed atoms in atomspace --> Boost STI/LTI --> 
Set Memory Capacity

I assume you got that from on of the experiments. You obviously don't want 
to generate face sentences so we can ignore that. Feeding Atoms into the 
AtomSpace would be done by the NLP Pipeline. Boosting STI/LTI would be done 
when they become relevant i.e they just entered the AtomSpace or NLP found 
them to be useful. (again ask misgana about his implementation of this). 
Setting the Memory Capacity you probably don't have to do but it's just a 
value in the config file.

If you have some more specific question about how ECAN works (i.e. 
spreading of STI , rent collection , setting of the AFB) i can answer those.


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