Sure, but it is a consistent extension; {A}-statements have a strongly
NARS-like semantics, so we know they won't just mess everything up.
On Mon, Sep 22, 2008 at 11:31 AM, Ben Goertzel <[EMAIL PROTECTED]> wrote:
>
> Of course ... but then you are not doing NARS inference anymore...
>
> On Mon, Sep 22, 2008 at 8:25 AM, Abram Demski <[EMAIL PROTECTED]> wrote:
>>
>> 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
>> > ________________________________
>> > agi | Archives | Modify Your Subscription
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>>
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>
>
> --
> Ben Goertzel, PhD
> CEO, Novamente LLC and Biomind LLC
> Director of Research, SIAI
> [EMAIL PROTECTED]
>
> "Nothing will ever be attempted if all possible objections must be first
> overcome " - Dr Samuel Johnson
>
>
> ________________________________
> agi | Archives | Modify Your Subscription
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