The definition I proposed is based on the unknown formulaic definition of an
observer, a program with resources, that estimates in some way, possibly
100% wrong universally but for local purposes it has utility. The observer
in this case could be us defining what the similarity comparison of the
number is even if a number could be a googleplex of digits. 

An example would be scene classification that parses scene contents based on
an observed or estimated/computed complexity. Is that estimated complexity
not a K'() as in Michael's case? That scene A could be similar to another
scene B based on a complexity derived with available computed resources.

John

-----Original Message-----
From: Ben Goertzel [mailto:[email protected]] 
Sent: Friday, February 21, 2014 8:53 PM
To: AGI
Subject: Re: [agi] Numeric Similarity

Kolmogorov complexity is not accurately estimable in either worst case or
average case, in any feasible (or even computable) way ...

On Sat, Feb 22, 2014 at 8:27 AM,  <[email protected]> wrote:
>
> K() would be the Kolmogorov complexity. Matt Mahoney always complains 
> that the K Complexity is not computable but never talks about it being
estimable.
>
> John
>
>
>
> On 2014-02-21 19:05, Piaget Modeler wrote:
>>
>> I like this too. How would one define K ?
>>
>> ~PM
>>
>> -------------------------
>>
>> From: [email protected]
>> To: [email protected]
>> Subject: RE: [agi] Numeric Similarity
>> Date: Fri, 21 Feb 2014 16:18:16 -0500
>>
>> It could also be something like this:
>>
>> Similarity(A, B) = 1 / (1 + |K'(A) - K'(B)|)
>>
>> where K'(A) is the estimated complexity of A. The K' function is 
>> dependent on observer formulaics and resources.
>>
>> John
>>
>> FROM: Piaget Modeler [mailto:[email protected]]
>> SENT: Friday, February 21, 2014 3:46 PM
>> TO: AGI
>> SUBJECT: RE: [agi] Numeric Similarity
>>
>>
>> Actually Aaron Hosford just recommended
>>
>>  1 / (1 + | a - b | )
>>
>> Which I like much better.
>>
>> Thanks Aaron.
>>
>> ~PM
>>
>> -------------------------
>>
>>
>> From: [email protected]
>> To: [email protected]
>> Subject: RE: [agi] Numeric Similarity
>> Date: Fri, 21 Feb 2014 06:02:46 -0800
>>
>> Thanks to all respondents.
>>
>> In the end I found a classic numeric similarity metric: 1 - | a - b |
>>
>> It's not ideal since numeric scores can dominate other attribute 
>> scores.
>>
>> Ergo, I have to devise a good weighting scheme.
>>
>> Nothing's perfect I suppose.
>>
>> Cheers,
>>
>> ~ PM
>>
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--
Ben Goertzel, PhD
http://goertzel.org

"In an insane world, the sane man must appear to be insane". -- Capt.
James T. Kirk


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