opencog-dev team mailing list archive

["Ben Goertzel" <ben@xxxxxxxxxxxx>]

Yes that is correct, Joel ... that is what I meant to suggest in the
first place, sorry I wasn't clear!!

ben


On Thu, Jul 3, 2008 at 3:34 AM, Joel Pitt <joel.pitt@xxxxxxxxx> wrote:
> Hi Ben,
>
> Quick question about using Markov matrices for Importance spread.
>
> In the initial suggestion you wrote:
>> I'm wondering if we could think about this in terms of Markov matrices.
>>
>> Let the variable p_i equal the "probability that Atom A_i is selected",
>> which should be proportional to its STI
>>
>> Then the Hebbian and InverseHebbianLinks should determine the
>> transition probabilities p_ij, right?   (The probability that A_i is selected,
>> given that A_j was selected)
>>
>> Then, we have a Markov matrix M.
>>
>> We can find its inverse matrix N
>>
>> Perhaps we then want to multiply q=N p, where p=(p_1,...,p_n)
>>
>> ... and set the STI levels equal to the entries of q?
>
> However, after brushing up on some linear algebra, it seems to me that
> we don't need to find the inverse matrix at all.
>
> Representing spread as a Markov process, each potential
> source/destination atom is a state which a portion of STI can be in.
> p_n represents the initial state (i.e. the distribution of STI across
> atoms) and we merely have to multiply p by M to get the next state
> p_(n+1)
>
> Make sense?
>
> J
>
>
> The Markov or transition matrix is simply multiplied by the
> distribution of importance across states.
>



-- 
Ben Goertzel, PhD
CEO, Novamente LLC and Biomind LLC
Director of Research, SIAI
ben@xxxxxxxxxxxx

"Nothing will ever be attempted if all possible objections must be
first overcome " - Dr Samuel Johnson