Great, I'm glad you are interested in an experiment.

A reasonable step would be for Nil to send you some real PLN and URE
> inference histories and see what your visualizer does with them...
>

Sure, JSON history files would be perfect (we could make this a standard
communication pipe between reasoner and visualizer), but I guess I can
somehow manage any existing format you are already used to. You know my
mail.

Currently, the library is AJAX-ing an XML tree structure and standard HTMLs
as input, with possibility of using php or other server side scripting
technology to interface the input files you pass over here. Would that be
ok? I have to mention, with some additional effort, there could be other
input options such as reading and evaluating JSON file wrapped into
javascript source code file. I'm telling this because this would exclude
the requirement of running a HTTP and php server, but if it is not
necessary, I'd like to avoid this step and do the php trick.

pon, 12. srp 2021. u 20:30 Ben Goertzel <[email protected]> napisao je:

> A reasonable step would be for Nil to send you some real PLN and URE
> inference histories and see what your visualizer does with them...
>
> On Mon, Jul 12, 2021, 10:59 AM Ivan V. <[email protected]> wrote:
>
>> I made a small infinity test <http://ocog.atspace.cc/infinite/> too.
>> Each parent virtually has an infinite number of children. Rolling ovals
>> around, zooming ovals in, zooming ovals out, ... Surely it's not exactly
>> perfect, but I could live with it.
>>
>> pon, 12. srp 2021. u 17:48 Linas Vepstas <[email protected]>
>> napisao je:
>>
>>> Hi Ivan,
>>>
>>> On Mon, Jul 12, 2021 at 6:00 AM Ivan V. <[email protected]> wrote:
>>>
>>>>
>>>> Thank you for asking, and my thoughts are pretty obvious. As I
>>>> understand, URE and PLN are all about proofs, so my thoughts may go in that
>>>> direction. Suppose we have a natural deduction proof composition:
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> *  ---   ---   ---     ---   ---   ---     ---   ---   ---   I     J
>>>>   K       L     M     N       P     Q     R -----------------
>>>> -----------------   -----------------         A                   B
>>>>           C-----------------------------------------------------------
>>>>                        X*
>>>>
>>>> You can already see the tree-like composition, but as it may span over
>>>> a very wide and tall area, it may be required to represent it within an
>>>> on-demand scaling system. This example <http://ocog.atspace.cc/>
>>>> roughly shows what I have imagined for proof representation. In the example
>>>> you can play with ovals, dragging them around and in or out the central
>>>> area, zooming proof parts of the current interest. Notice how it is
>>>> possible to represent and navigate nearly infinite length proofs, assuming
>>>> enough memory space.
>>>>
>>>
>>> Re: navigating trees: if you don't already know this, then I suggest
>>> that you really, really should study hyperbolic rotations aka mobius
>>> transformations on the poincare disk. They implement your example.  I
>>> recall seeing a demo of this at SIGGRAPH two or three decades ago. As you
>>> pan around on the hyperbolic disk, different parts of the graph get
>>> magnified at the center. And, like an MC Escher print, the rest of the
>>> graph remains compressed at the edges.
>>>
>>> For scale-free networks, this doesn't work. And from what I can tell,
>>> learning really does result in something close to scale-free networks.
>>> What this means in practice is that there's one vertex with a million edges
>>> coming off of it.  There are two, with half-a-million each. Four, with a
>>> quarter-million each, and so on. So almost all vertexes have just a handful
>>> of edges connected to them, but as you move around, from vertex to vertex,
>>> you bump into these monsters. And you can't really draw them: try drawing a
>>> vertex with a thousand edges on your 2Kx2K monitor: most of those edges
>>> will be less than one pixel from each-other. It'll be just a big blob.
>>>
>>> It's important to "eat your own dog-food", as they say, or "smoke your
>>> own dope": use your own code to solve actual, real-world problems. This
>>> very quickly highlights where all that beautiful theory doesn't quite work
>>> out in practice.
>>>
>>> --linas
>>>
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