On Sat, Oct 25, 2008 at 12:40 PM, Russell Wallace
<[EMAIL PROTECTED]> wrote:
>
>> What I see as potential way of AI in program analysis is cracking
>> abstract interpretation, automatically inventing invariants and
>> proving that they hold, using these invariants to interface between
>> results of analysis in different parts of the program and to answer
>> the questions posed before analysis. This task has interesting
>> similarities with summarizing world-model, where you need to perform
>> inference on a huge network of elements of physical reality (start
>> with physical laws, if they were simple, or chess rules in a chess
>> game), basically by dynamically applying summarizing events, matching
>> simplified models.
>
> Yes, that's the sort of thing I have in mind.

Note that people are working on this specific technical problem for 30
years, (see the scary amount of work by Cousot's lab,
http://www.di.ens.fr/~cousot/COUSOTpapers/ ), and they are still
tackling fixed invariants, finding ways to summarize program code as
transformations on domains containing families of assertions about
program state, to handle loops, to work with more features of
programming languages they analyze. And it all is still imprecise and
is able to find only relatively weak assertions. Open-ended invention
of assertions to reflect the effect of program code in a more adaptive
way in even on a horizon.


>> But it all looks almost AI-complete.
>
> It's a very hard problem, but it's a long way short of AI complete. I
> think it's worth aiming for as an intermediate stage between the
> current state of the art and "good morning Dr. Chandra".
>

I don't know, it looks like a long way there. I'm currently shifting
towards probabilistic analysis of huge formal systems in my thinking
about AI (which is why chess looks interesting again, in an entirely
new light). Maybe I'll understand this area better in months to come.


-- 
Vladimir Nesov
[EMAIL PROTECTED]
http://causalityrelay.wordpress.com/


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agi
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