2008/12/26 Matt Mahoney <[email protected]>:
>
> Humans are very good at predicting sequences of symbols, e.g. the next word 
> in a text stream.

Why not have that as your problem domain, instead of text compression?

>
> Most compression tests are like defining intelligence as the ability to catch 
> mice. They measure the ability of compressors to compress specific files. 
> This tends to lead to hacks that are tuned to the benchmarks. For the generic 
> intelligence test, all you know about the source is that it has a Solomonoff 
> distribution (for a particular machine). I don't know how you could make the 
> test any more generic.

It seems to me that you and Hutter are interested in a problem domain
that consists of:

1. generating random turing machines

2. running them to produce output

3. feeding the output as input to another program P, which will then
guess future characters based on previous ones

4. having P use these guesses to do compression

May I suggest that instead you modify this problem domain by:

(a) remove clause 1 -- it's not fundamentally interesting that output
comes from a turing machine. Maybe instead make output come from a
program (written by humans and interesting to humans) in a normal
programming language that people would actually use to write code in

(b) remove clause 4 -- compression is a bit of a red herring here,
what's important is to predict future output based on past output.

IMO if you made these changes, your problem domain would be a more useful one.

While you're at it you may want to change the size of the "chunks" in
each item of prediction, from characters to either strings or
s-expressions. Though doing so doesn't fundamentally alter the
problem.

-- 
Philip Hunt, <[email protected]>
Please avoid sending me Word or PowerPoint attachments.
See http://www.gnu.org/philosophy/no-word-attachments.html


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agi
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