It would be possible to get what you want in the setting, by allowing
some probabilistic manipulations not done in NARS. The node
probability you want in this case could be simulated by talking about
the probability distribution of sentences of the form "X is the author
of a book". We can give this a low prior probability. Since the system
manipulates likelihoods, it won't notice; but if we manipulate
probabilities, it would.
Perhaps a more satisfying answer would be to introduce a new operator
into the system, {A}, that simulates the node probability; or more
specifically, it represents the average truth-value distribution of
statements that have A on one side or the other. So, it has a 'par'
value just like inheritance statements do. If there was evidence for a
low par, there would be an effect in the direction you want. (It might
be way too small, though?)
--Abram
On Sun, Sep 21, 2008 at 10:46 PM, Ben Goertzel <[EMAIL PROTECTED]> wrote:
>
>
> On Sun, Sep 21, 2008 at 10:43 PM, Abram Demski <[EMAIL PROTECTED]>
> wrote:
>>
>> The calculation in which I sum up a bunch of pairs is equivalent to
>> doing NARS induction + abduction with a final big revision at the end
>> to combine all the accumulated evidence. But, like I said, I need to
>> provide a more explicit justification of that calculation...
>
> As an example inference, consider
>
> Ben is an author of a book on AGI <tv1>
> This dude is an author of a book on AGI <tv2>
> |-
> This dude is Ben <tv3>
>
> versus
>
> Ben is odd <tv1>
> This dude is odd <tv2>
> |-
> This dude is Ben <tv4>
>
> (Here each of the English statements is a shorthand for a logical
> relationship that in the AI systems in question is expressed in a formal
> structure; and the notations like <tv1> indicate uncertain truth values
> attached to logical relationships, In both NARS and PLN, uncertain truth
> values have multiple components, including a "strength" value that denotes a
> frequency, and other values denoting confidence measures. However, the
> semantics of the strength values in NARS and PLN are not identical.)
>
> Doing these two inferences in NARS you will get
>
> tv3.strength = tv4.strength
>
> whereas in PLN you will not, you will get
>
> tv3.strength >> tv4.strength
>
> The difference between the two inference results in the PLN case results
> from the fact that
>
> P(author of book on AGI) << P(odd)
>
> and the fact that PLN uses Bayes rule as part of its approach to these
> inferences.
>
> So, the question is, in your probabilistic variant of NARS, do you get
>
> tv3.strength = tv4.strength
>
> in this case, and if so, why?
>
> thx
> ben
> ________________________________
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